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The devil wears Pravda

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/the-devil-wears-pravda.html

Classic Bond villain, Elon Musk, has a new plan to create a website dedicated to measuring the credibility and adherence to “core truth” of journalists. He is, without any sense of irony, going to call this “Pravda”. This is not simply wrong but evil.

Musk has a point. Journalists do suck, and many suck consistently. I see this in my own industry, cybersecurity, and I frequently criticize them for their suckage.

But what he’s doing here is not correcting them when they make mistakes (or what Musk sees as mistakes), but questioning their legitimacy. This legitimacy isn’t measured by whether they follow established journalism ethics, but whether their “core truths” agree with Musk’s “core truths”.

An example of the problem is how the press fixates on Tesla car crashes due to its “autopilot” feature. Pretty much every autopilot crash makes national headlines, while the press ignores the other 40,000 car crashes that happen in the United States each year. Musk spies on Tesla drivers (hello, classic Bond villain everyone) so he can see the dip in autopilot usage every time such a news story breaks. He’s got good reason to be concerned about this.

He argues that autopilot is safer than humans driving, and he’s got the statistics and government studies to back this up. Therefore, the press’s fixation on Tesla crashes is illegitimate “fake news”, titillating the audience with distorted truth.

But here’s the thing: that’s still only Musk’s version of the truth. Yes, on a mile-per-mile basis, autopilot is safer, but there’s nuance here. Autopilot is used primarily on freeways, which already have a low mile-per-mile accident rate. People choose autopilot only when conditions are incredibly safe and drivers are unlikely to have an accident anyway. Musk is therefore being intentionally deceptive comparing apples to oranges. Autopilot may still be safer, it’s just that the numbers Musk uses don’t demonstrate this.

And then there is the truth calling it “autopilot” to begin with, because it isn’t. The public is overrating the capabilities of the feature. It’s little different than “lane keeping” and “adaptive cruise control” you can now find in other cars. In many ways, the technology is behind — my Tesla doesn’t beep at me when a pedestrian walks behind my car while backing up, but virtually every new car on the market does.

Yes, the press unduly covers Tesla autopilot crashes, but Musk has only himself to blame by unduly exaggerating his car’s capabilities by calling it “autopilot”.

What’s “core truth” is thus rather difficult to obtain. What the press satisfies itself with instead is smaller truths, what they can document. The facts are in such cases that the accident happened, and they try to get Tesla or Musk to comment on it.

What you can criticize a journalist for is therefore not “core truth” but whether they did journalism correctly. When such stories criticize “autopilot”, but don’t do their diligence in getting Tesla’s side of the story, then that’s a violation of journalistic practice. When I criticize journalists for their poor handling of stories in my industry, I try to focus on which journalistic principles they get wrong. For example, the NYTimes reporters do a lot of stories quoting anonymous government sources in clear violation of journalistic principles.

If “credibility” is the concern, then it’s the classic Bond villain here that’s the problem: Musk himself. His track record on business statements is abysmal. For example, when he announced the Model 3 he claimed production targets that every Wall Street analyst claimed were absurd. He didn’t make those targets, he didn’t come close. Model 3 production is still lagging behind Musk’s twice adjusted targets.

https://www.bloomberg.com/graphics/2018-tesla-tracker/

So who has a credibility gap here, the press, or Musk himself?

Not only is Musk’s credibility problem ironic, so is the name he chose, “Pravada”, the Russian word for truth that was the name of the Soviet Union Communist Party’s official newspaper. This is so absurd this has to be a joke, yet Musk claims to be serious about all this.

Yes, the press has a lot of problems, and if Musk were some journalism professor concerned about journalists meeting the objective standards of their industry (e.g. abusing anonymous sources), then this would be a fine thing. But it’s not. It’s Musk who is upset the press’s version of “core truth” does not agree with his version — a version that he’s proven time and time again differs from “real truth”.

Just in case Musk is serious, I’ve already registered “www.antipravda.com” to start measuring the credibility of statements by billionaire playboy CEOs. Let’s see who blinks first.

I stole the title, with permission, from this tweet:

Some notes on eFail

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/some-notes-on-efail.html

I’ve been busy trying to replicate the “eFail” PGP/SMIME bug. I thought I’d write up some notes.

PGP and S/MIME encrypt emails, so that eavesdroppers can’t read them. The bugs potentially allow eavesdroppers to take the encrypted emails they’ve captured and resend them to you, reformatted in a way that allows them to decrypt the messages.

Disable remote/external content in email

The most important defense is to disable “external” or “remote” content from being automatically loaded. This is when HTML-formatted emails attempt to load images from remote websites. This happens legitimately when they want to display images, but not fill up the email with them. But most of the time this is illegitimate, they hide images on the webpage in order to track you with unique IDs and cookies. For example, this is the code at the end of an email from politician Bernie Sanders to his supporters. Notice the long random number assigned to track me, and the width/height of this image is set to one pixel, so you don’t even see it:

Such trackers are so pernicious they are disabled by default in most email clients. This is an example of the settings in Thunderbird:

The problem is that as you read email messages, you often get frustrated by the fact the error messages and missing content, so you keep adding exceptions:

The correct defense against this eFail bug is to make sure such remote content is disabled and that you have no exceptions, or at least, no HTTP exceptions. HTTPS exceptions (those using SSL) are okay as long as they aren’t to a website the attacker controls. Unencrypted exceptions, though, the hacker can eavesdrop on, so it doesn’t matter if they control the website the requests go to. If the attacker can eavesdrop on your emails, they can probably eavesdrop on your HTTP sessions as well.

Some have recommended disabling PGP and S/MIME completely. That’s probably overkill. As long as the attacker can’t use the “remote content” in emails, you are fine. Likewise, some have recommend disabling HTML completely. That’s not even an option in any email client I’ve used — you can disable sending HTML emails, but not receiving them. It’s sufficient to just disable grabbing remote content, not the rest of HTML email rendering.

I couldn’t replicate the direct exfiltration

There rare two related bugs. One allows direct exfiltration, which appends the decrypted PGP email onto the end of an IMG tag (like one of those tracking tags), allowing the entire message to be decrypted.

An example of this is the following email. This is a standard HTML email message consisting of multiple parts. The trick is that the IMG tag in the first part starts the URL (blog.robertgraham.com/…) but doesn’t end it. It has the starting quotes in front of the URL but no ending quotes. The ending will in the next chunk.

The next chunk isn’t HTML, though, it’s PGP. The PGP extension (in my case, Enignmail) will detect this and automatically decrypt it. In this case, it’s some previous email message I’ve received the attacker captured by eavesdropping, who then pastes the contents into this email message in order to get it decrypted.

What should happen at this point is that Thunderbird will generate a request (if “remote content” is enabled) to the blog.robertgraham.com server with the decrypted contents of the PGP email appended to it. But that’s not what happens. Instead, I get this:

I am indeed getting weird stuff in the URL (the bit after the GET /), but it’s not the PGP decrypted message. Instead what’s going on is that when Thunderbird puts together a “multipart/mixed” message, it adds it’s own HTML tags consisting of lines between each part. In the email client it looks like this:

The HTML code it adds looks like:

That’s what you see in the above URL, all this code up to the first quotes. Those quotes terminate the quotes in the URL from the first multipart section, causing the rest of the content to be ignored (as far as being sent as part of the URL).

So at least for the latest version of Thunderbird, you are accidentally safe, even if you have “remote content” enabled. Though, this is only according to my tests, there may be a work around to this that hackers could exploit.

STARTTLS

In the old days, email was sent plaintext over the wire so that it could be passively eavesdropped on. Nowadays, most providers send it via “STARTTLS”, which sorta encrypts it. Attackers can still intercept such email, but they have to do so actively, using man-in-the-middle. Such active techniques can be detected if you are careful and look for them.
Some organizations don’t care. Apparently, some nation states are just blocking all STARTTLS and forcing email to be sent unencrypted. Others do care. The NSA will passively sniff all the email they can in nations like Iraq, but they won’t actively intercept STARTTLS messages, for fear of getting caught.
The consequence is that it’s much less likely that somebody has been eavesdropping on you, passively grabbing all your PGP/SMIME emails. If you fear they have been, you should look (e.g. send emails from GMail and see if they are intercepted by sniffing the wire).

You’ll know if you are getting hacked

If somebody attacks you using eFail, you’ll know. You’ll get an email message formatted this way, with multipart/mixed components, some with corrupt HTML, some encrypted via PGP. This means that for the most part, your risk is that you’ll be attacked only once — the hacker will only be able to get one message through and decrypt it before you notice that something is amiss. Though to be fair, they can probably include all the emails they want decrypted as attachments to the single email they sent you, so the risk isn’t necessarily that you’ll only get one decrypted.
As mentioned above, a lot of attackers (e.g. the NSA) won’t attack you if its so easy to get caught. Other attackers, though, like anonymous hackers, don’t care.
Somebody ought to write a plugin to Thunderbird to detect this.

Summary

It only works if attackers have already captured your emails (though, that’s why you use PGP/SMIME in the first place, to guard against that).
It only works if you’ve enabled your email client to automatically grab external/remote content.
It seems to not be easily reproducible in all cases.
Instead of disabling PGP/SMIME, you should make sure your email client hast remote/external content disabled — that’s a huge privacy violation even without this bug.

Notes: The default email client on the Mac enables remote content by default, which is bad:

Leaking securely, for White House staffers

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/how-to-leak-securely-for-white-house.html

Spencer Ackerman has this interesting story about a guy assigned to crack down on unauthorized White House leaks. It’s necessarily light on technical details, so I thought I’d write up some guesses, either as a guide for future reporters asking questions, or for people who want to better know the risks when leak information.

It should come as no surprise that your work email and phone are already monitored. They can get every email you’ve sent or received, even if you’ve deleted it. They can get every text message you’ve sent or received, the metadata of every phone call sent or received, and so forth.
To a lesser extent, this also applies to your well-known personal phone and email accounts. Law enforcement can get the metadata (which includes text messages) for these things without a warrant. In the above story, the person doing the investigation wasn’t law enforcement, but I’m not sure that’s a significant barrier if they can pass things onto the Secret Service or something.
The danger here isn’t that you used these things to leak, it’s that you’ve used these things to converse with the reporter before you made the decision to leak. That’s what happened in the Reality Winner case: she communicated with The Intercept before she allegedly leaked a printed document to them via postal mail. While it wasn’t conclusive enough to convict her, the innocent emails certainly put the investigators on her trail.
The path to leaking often starts this way: innocent actions before the decision to leak was made that will come back to haunt the person afterwards. That includes emails. That also includes Google searches. That includes websites you visit (like this one). I’m not sure how to solve this, except that if you’ve been in contact with The Intercept, and then you decide to leak, send it to anybody but The Intercept.
By the way, the other thing that caught Reality Winner is the records they had of her accessing files and printing them on a printer. Depending where you work, they may have a record of every file you’ve accessed, every intranet page you visited. Because of the way printers put secret dots on documents, investigators know precisely which printer and time the document leaked to The Intercept was printed.
Photographs suffer the same problem: your camera and phone tag the photographs with GPS coordinates and time the photograph was taken, as well as information about the camera. This accidentally exposed John McAfee’s hiding location when Vice took pictures of him a few years ago. Some people leak by taking pictures of the screen — use a camera without GPS for this (meaning, a really old camera you bought from a pawnshop).
These examples should impress upon you the dangers of not understanding technology. As soon as you do something to evade surveillance you know about, you may get caught by surveillance you don’t know about.
If you nonetheless want to continue forward, the next step may be to get a “burner phone”. You can get an adequate Android “prepaid” phone for cash at the local Walmart, electronics store, or phone store.
There’s some problems with such phones, though. They can often be tracked back to the store that sold them, and the store will have security cameras that record you making the purchase. License plate readers and GPS tracking on your existing phone may also place you at that Walmart.
I don’t know how to resolve these problems. Perhaps the best is grow a beard and on the last day of your vacation, color your hair, take a long bike/metro ride (without your existing phone) to a store many miles away and pick up a phone, then shave and change your color back again. I don’t know — there’s a good chance any lame attempt you or I might think of has already been experienced by law enforcement, so they are likely ahead of you. Maybe ask your local drug dealer where they get their burner phones, and if they can sell you one. Of course, that just means when they get caught for drug dealing, they can reduce their sentence by giving up the middle class person who bought a phone from them.
Lastly, they may age out old security videos, so simply waiting six months before using the phone might work. That means prepaying for an entire year.
Note that I’m not going to link to examples of cheap burner phones on this page. Web browsers will sometimes prefetch some information from links in a webpage, so simply including links in this page can condemn you as having interest in burner phones. You are already in enough trouble for having visited this web page.
Burner phones have GPS. Newer the technology, like the latest Android LTE phones, have pretty accurate GPS that the police can query (without a warrant). If you take the phone home and turn it on, they’ll then be able to trace back the phone to your home. Carrying the phone around with you has the same problem, with the phone’s location correlating with your existing phone (which presumably you also carry) or credit card receipts. Rumors are that Petraeus was partly brought down by tracking locations where he used his credit card, namely, matching the hotel he was in with Internet address information.
Older phones that support 3G or even 2G have poorer GPS capabilities. They’ll still located you to the nearest cell tower, but not as accurately to your exact location.
A better strategy than a burner phone would be a burner laptop computer used with WiFi. You can get a cheap one for $200 at Amazon.com. My favorite are the 11 inch ones with a full sized keyboard and Windows 10. Better yet, get an older laptop for cash from a pawn shop.
You can install chat apps on this like “Signal Desktop”, “Wire Desktop”,  or “WhatsApp” that will allow you to securely communicate. Or use “Discord”, which isn’t really encrypted, but it’s popular among gamers so therefore less likely to stand out. You can sit in a bar with free WiFi and a USB headset and talk to reporters without having a phone. If the reporter you want to leak to doesn’t have those apps (either on their own laptop or phone) then you don’t want to talk to them.
Needless to say, don’t cross the streams. Don’t log onto your normal accounts like Facebook. If you create fake Facebook accounts, don’t follow the same things. Better yet, configure your browser to discard all information (especially “cookies”) every time you log off, so you can’t be tracked. Install ad blockers, or use the “Brave” web browser, to remove even more trackers. A common trick among hackers is to change the “theme” to a red background, as a constant subliminal reminder that you using your dangerous computer, and never to do anything that identifies the real you.
Put tape over the camera. I’m not sure it’s a really big danger, but put tape over the camera. If they infect you enough to get your picture, they’ve also infected you enough to record any audio on your computer. Remember that proper encryption is end-to-end (they can’t eavesdrop in transit), but if they hack the ends (your laptop, or the reporter’s) they can still record the audio.
Note that when your burner laptop is in “sleep” mode, it can still be talking to the local wifi. Before taking it home, make sure it’s off. Go into the settings and configure it so that when the lid is closed, the computer is turned completely off.
It goes without saying: don’t use that burner laptop from home. Luckily, free wifi is everyone, so the local cafe, bar, or library can be used.
The next step is to also use a VPN or Tor to mask your Internet address. If there’s an active investigation into the reporter, they’ll get the metadata, the Internet address of the bar/cafe you are coming from. A good VPN provider or especially Tor will stop this. Remember that these providers increase latency, making phone calls a bit harder, but they are a lot safer.
Remember that Ross Ulbricht (owner of dark website market Silk Road) was caught in a library. They’d traced back his Internet address and grabbed his laptop out of his hands. Having it turn off (off off, not sleep off) when the lid is closed is one way to reduce this risk. Configuring your web browser to flush all cookies and passwords on restart is another. If they catch you in mid conversation with your secret contact, though, they’ll at least be able to hear your side of the conversation, and know who you are talking to.
The best measure, though it takes some learning, is “Tails live”. It’s a Linux distribution preconfigured with Tor and various secure chat apps that’ll boot from the USB or SD card. When you turn off the computer, nothing will be saved, so there will be no evidence saved to the disk for investigators to retrieve later.
While we are talking about Tor, it should be noted that many news organizations (NYTimes, Washington Post, The Intercept, etc.) support “SecureDrop” accessed only through Tor for receiving anonymous tips. Burner laptops you use from bars from Tails is the likely your most secure way of doing things.
Summary

The point of this post was not to provide a howto guide, but to discuss many of the technological issues involved. In a story about White House people investigating leaks, I’d like to see something in this technological direction. I’d like to know exactly how they were investigating leaks. Certainly, they were investigating all work computers, accounts, and phones. Where they also able to get to non-work computers, accounts, phones? Did they have law enforcement powers? What could they do about burner phones and laptops?
In any case, if you do want a howto guide, the discussion above should put some fear into you how easily you can inadvertently make a mistake.

Съвместима ли е таксата за радио и телевизия с правото на ЕС

Post Syndicated from nellyo original https://nellyo.wordpress.com/2018/05/13/fee_psm/

През март  2018 г. Oberverwaltungsgericht Rheinland-Pfalz (Върховен административен съд на Рейнланд-Пфалц – OVG Rheinland-Pfalz) решава, че таксата за радио и телевизия в Германия е съвместима с правото на ЕС (дело № 7 A 11938/17) . Съдът отхвърля тезата, че таксата е несъвместима с правото на ЕС, тъй като предоставя на обществените радио- и телевизионни доставчици на медийни услуги несправедливо предимство пред техните частни конкуренти.

Съдът посочва, че през 2016 г. Bundesverwaltungsgericht (Федерален административен съд – BVerwG) вече е установил съответствието на таксата  – в новата й форма, въведена през 2013 г. – с правото на ЕС (решение от 18 март 2016 г., BVerwG 6 С 6.15). Съгласно това решение въвеждането на таксата  не изисква съгласието на Европейската комисия и е с съвместимо с Директивата за аудиовизуалните медийни услуги.  Обществените и частните радио- и телевизионни оператори  неизбежно ще бъдат финансирани по различни начини. Това обаче не означава непременно, че обществените радио- и телевизионни оператори са получили несправедливо предимство, тъй като за разлика от частните радио- и телевизионни оператори те са подложени на много по-ограничителни правила за рекламиране и следователно са финансово зависими от таксата.

Междувременно Landgericht Tübingen (Районен съд в Тюбинген, решение от 3 август 2017 г., дело № 5 T 246/17 и др.) е постановил, че таксата  нарушава правото на ЕС  – и в резултат има подадено преюдициално запитване до Съда на ЕС –  дело  С-492/17.

 

Преюдициални въпроси:

1)

Несъвместим ли е с правото на Съюза националният Gesetz vom 18.10.2011 zur Geltung des Rundfunkbeitragsstaatsvertrags (RdFunkBeitrStVtrBW) vom 17 Dezember 2010 (Закон от 18 октомври 2011 г. за прилагане на Държавния договор за вноската за радио- и телевизионно разпространение от 17 декември 2010 г., наричан по-нататък „RdFunkBeitrStVtrBW“) на провинция Баден-Вюртемберг, последно изменен с член 4 от Neunzehnter Rundfunkänderungsstaatsvertrag (Деветнадесети държавен договор за изменение на Държавните договори за радио- и телевизионно разпространение) от 3 декември 2015 г. (Закон от 23 февруари 2016 г., GBl. стр. 126, 129), поради това че вноската, събирана от 1 януари 2013 г. съгласно този закон безусловно по принцип от всяко живеещо в германската федерална провинция Баден-Вюртемберг пълнолетно лице в полза на радио- и телевизионните оператори SWR и ZDF, представлява помощ, която противоречи на правото на Съюза и предоставя по-благоприятно третиране само в полза на тези обществени радио- и телевизионни оператори спрямо частни радио- и телевизионни оператори? Трябва ли членове 107 и 108 ДФЕС да се тълкуват в смисъл, че за Закона за вноската за радио- и телевизионно разпространение е трябвало да се получи разрешението на Комисията и поради липсата на разрешение той е невалиден?

2)

Трябва ли член 107 ДФЕС, съответно член 108 ДФЕС да се тълкува в смисъл, че в обхвата му попада правна уредба, установена в националния закон „RdFunkBeitrStVtrBW“, която предвижда, че по принцип от всяко живеещо в Баден-Вюртемберг пълнолетно лице безусловно се събира вноска в полза само на държавни/обществени радио- и телевизионни оператори, поради това че тази вноска съдържа противоречаща на правото на Съюза и предоставяща по-благоприятно третиране помощ с цел изключването по технически причини на оператори от държави от Европейския съюз, доколкото вноските са предназначени да се използват за създаването на конкурентен начин на пренос (монопол върху DVB-T2), без да е предвидено той да се използва от чуждестранни оператори? Трябва ли член 107 ДФЕС, съответно член 108 ДФЕС да се тълкува в смисъл, че в обхвата му попадат не само преки субсидии, но и други релевантни от икономическа гледна точка привилегии (право на издаване на изпълнителен лист, правомощия за предприемане на действия както в качеството на стопанско предприятие, така и в качеството на орган, поставяне в по-благоприятно положение при изчисляването на дълговете)?

3)

Съвместимо ли е с принципа на равно третиране и със забраната за предоставящи привилегии помощи положение, при което на основание национален закон на провинция Баден-Вюртемберг германски телевизионен оператор, който се урежда от нормите на публичното право и има предоставени правомощия на орган, но същевременно се конкурира с частни радио- и телевизионни оператори на рекламния пазар, е привилегирован в сравнение с тези оператори поради това че не трябва като частните конкуренти да иска по общия съдебен ред да му бъде издаден изпълнителен лист за вземанията му срещу зрителите, преди да може да пристъпи към принудително изпълнение, а самият той има право, без участието на съд, да издаде титул, който същевременно му дава право на принудително изпълнение?

4)

Съвместимо ли е с член 10 от ЕКПЧ /член [11] от Хартата на основните права (свобода на информация) положение, при което държава членка предвижда в национален закон на провинция Баден-Вюртемберг, че телевизионен оператор, на който са предоставени правомощия на орган, има право да изисква плащането на вноска от всяко живеещо в зоната на радио- и телевизионното излъчване пълнолетно лице за целите на финансирането на точно този оператор, при неплащането на която е предвидена глоба, независимо дали това лице въобще разполага с приемник или само използва услугите на други, а именно чуждестранни или други, частни оператори?

5)

Съвместим ли е националният закон „RdFunkBeitrStVtrBW“, и по-специално членове 2 и 3, с установените в правото на Съюза принципи на равно третиране и на недопускане на дискриминация в положение, при което вноската, която следва да се плаща безусловно от всеки жител за целите на финансирането на обществен телевизионен оператор, налага на всяко лице, което само отглежда детето си, тежест в размер, многократно по-висок от сумата, дължима от лице, което живее в общо жилище с други хора? Следва ли Директива 2004/113/ЕО (1) да се тълкува в смисъл, че спорната вноска също попада в обхвата ѝ и че e достатъчно да е налице косвено поставяне в по-неблагоприятно положение, след като с оглед на реалните дадености 90 % от жените понасят по-голяма тежест?

6)

Съвместим ли националният закон „RdFunkBeitrStVtrBW“, и по-специално членове 2 и 3, с установените в правото на Съюза принципи на равно третиране и на недопускане на дискриминация в положение, при което вноската, която следва да се плаща безусловно от всеки жител за целите на финансирането на обществен телевизионен оператор, за нуждаещите се от второ жилище лица по свързана с работата причина е двойно по-голяма, отколкото за други работници?

7)

Съвместим ли е националният закон „RdFunkBeitrStVtrBW“, и по-специално членове 2 и 3, с установените в правото на Съюза принципи на равно третиране и на недопускане на дискриминация и със свободата на установяване, ако вноската, която следва да се плаща безусловно от всеки жител за целите на финансирането на обществен телевизионен оператор, е уредена по такъв начин, че при еднаква възможност за приемане на радио- и телевизионно разпространение непосредствено преди границата със съседна държава от ЕС германски гражданин дължи вноската само поради мястото си на пребиваване, докато германският гражданин, живущ непосредствено от другата страна на границата, не дължи вноската, също както гражданинът на друга държава — членка на ЕС, който по свързани с работата причини трябва да се установи непосредствено от другата страна на вътрешна граница на ЕС, понася тежестта на вноската, но не и гражданинът на ЕС, живущ непосредствено преди границата, дори и никой от двамата да не се интересува от приемането на излъчванията на германския оператор?

Коментар по въпрос №4:  допуснат е въпрос за съвместимост с чл.10 от Конвенцията за правата на човека. Съдът за правата на човека вече се е произнасял, има съображения за недопустимост по сходно дело отпреди десетина години –  ето тук съм писала – вж Faccio v Italy – но нека да се произнесе и Съдът на ЕС.

И – отново за характера на таксата: ако  плащат и хората без приемник, това очевидно не е такса в смисъл цена за услуга, а данъчно вземане, по мое мнение това е тенденцията.

Чакаме решението на Съда на ЕС. Нека да се развива и множи практиката.

Securing Your Cryptocurrency

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/backing-up-your-cryptocurrency/

Securing Your Cryptocurrency

In our blog post on Tuesday, Cryptocurrency Security Challenges, we wrote about the two primary challenges faced by anyone interested in safely and profitably participating in the cryptocurrency economy: 1) make sure you’re dealing with reputable and ethical companies and services, and, 2) keep your cryptocurrency holdings safe and secure.

In this post, we’re going to focus on how to make sure you don’t lose any of your cryptocurrency holdings through accident, theft, or carelessness. You do that by backing up the keys needed to sell or trade your currencies.

$34 Billion in Lost Value

Of the 16.4 million bitcoins said to be in circulation in the middle of 2017, close to 3.8 million may have been lost because their owners no longer are able to claim their holdings. Based on today’s valuation, that could total as much as $34 billion dollars in lost value. And that’s just bitcoins. There are now over 1,500 different cryptocurrencies, and we don’t know how many of those have been misplaced or lost.



Now that some cryptocurrencies have reached (at least for now) staggering heights in value, it’s likely that owners will be more careful in keeping track of the keys needed to use their cryptocurrencies. For the ones already lost, however, the owners have been separated from their currencies just as surely as if they had thrown Benjamin Franklins and Grover Clevelands over the railing of a ship.

The Basics of Securing Your Cryptocurrencies

In our previous post, we reviewed how cryptocurrency keys work, and the common ways owners can keep track of them. A cryptocurrency owner needs two keys to use their currencies: a public key that can be shared with others is used to receive currency, and a private key that must be kept secure is used to spend or trade currency.

Many wallets and applications allow the user to require extra security to access them, such as a password, or iris, face, or thumb print scan. If one of these options is available in your wallets, take advantage of it. Beyond that, it’s essential to back up your wallet, either using the backup feature built into some applications and wallets, or manually backing up the data used by the wallet. When backing up, it’s a good idea to back up the entire wallet, as some wallets require additional private data to operate that might not be apparent.

No matter which backup method you use, it is important to back up often and have multiple backups, preferable in different locations. As with any valuable data, a 3-2-1 backup strategy is good to follow, which ensures that you’ll have a good backup copy if anything goes wrong with one or more copies of your data.

One more caveat, don’t reuse passwords. This applies to all of your accounts, but is especially important for something as critical as your finances. Don’t ever use the same password for more than one account. If security is breached on one of your accounts, someone could connect your name or ID with other accounts, and will attempt to use the password there, as well. Consider using a password manager such as LastPass or 1Password, which make creating and using complex and unique passwords easy no matter where you’re trying to sign in.

Approaches to Backing Up Your Cryptocurrency Keys

There are numerous ways to be sure your keys are backed up. Let’s take them one by one.

1. Automatic backups using a backup program

If you’re using a wallet program on your computer, for example, Bitcoin Core, it will store your keys, along with other information, in a file. For Bitcoin Core, that file is wallet.dat. Other currencies will use the same or a different file name and some give you the option to select a name for the wallet file.

To back up the wallet.dat or other wallet file, you might need to tell your backup program to explicitly back up that file. Users of Backblaze Backup don’t have to worry about configuring this, since by default, Backblaze Backup will back up all data files. You should determine where your particular cryptocurrency, wallet, or application stores your keys, and make sure the necessary file(s) are backed up if your backup program requires you to select which files are included in the backup.

Backblaze B2 is an option for those interested in low-cost and high security cloud storage of their cryptocurrency keys. Backblaze B2 supports 2-factor verification for account access, works with a number of apps that support automatic backups with encryption, error-recovery, and versioning, and offers an API and command-line interface (CLI), as well. The first 10GB of storage is free, which could be all one needs to store encrypted cryptocurrency keys.

2. Backing up by exporting keys to a file

Apps and wallets will let you export your keys from your app or wallet to a file. Once exported, your keys can be stored on a local drive, USB thumb drive, DAS, NAS, or in the cloud with any cloud storage or sync service you wish. Encrypting the file is strongly encouraged — more on that later. If you use 1Password or LastPass, or other secure notes program, you also could store your keys there.

3. Backing up by saving a mnemonic recovery seed

A mnemonic phrase, mnemonic recovery phrase, or mnemonic seed is a list of words that stores all the information needed to recover a cryptocurrency wallet. Many wallets will have the option to generate a mnemonic backup phrase, which can be written down on paper. If the user’s computer no longer works or their hard drive becomes corrupted, they can download the same wallet software again and use the mnemonic recovery phrase to restore their keys.

The phrase can be used by anyone to recover the keys, so it must be kept safe. Mnemonic phrases are an excellent way of backing up and storing cryptocurrency and so they are used by almost all wallets.

A mnemonic recovery seed is represented by a group of easy to remember words. For example:

eye female unfair moon genius pipe nuclear width dizzy forum cricket know expire purse laptop scale identify cube pause crucial day cigar noise receive

The above words represent the following seed:

0a5b25e1dab6039d22cd57469744499863962daba9d2844243fec 9c0313c1448d1a0b2cd9e230a78775556f9b514a8be45802c2808e fd449a20234e9262dfa69

These words have certain properties:

  • The first four letters are enough to unambiguously identify the word.
  • Similar words are avoided (such as: build and built).

Bitcoin and most other cryptocurrencies such as Litecoin, Ethereum, and others use mnemonic seeds that are 12 to 24 words long. Other currencies might use different length seeds.

4. Physical backups — Paper, Metal

Some cryptocurrency holders believe that their backup, or even all their cryptocurrency account information, should be stored entirely separately from the internet to avoid any risk of their information being compromised through hacks, exploits, or leaks. This type of storage is called “cold storage.” One method of cold storage involves printing out the keys to a piece of paper and then erasing any record of the keys from all computer systems. The keys can be entered into a program from the paper when needed, or scanned from a QR code printed on the paper.

Printed public and private keys

Printed public and private keys

Some who go to extremes suggest separating the mnemonic needed to access an account into individual pieces of paper and storing those pieces in different locations in the home or office, or even different geographical locations. Some say this is a bad idea since it could be possible to reconstruct the mnemonic from one or more pieces. How diligent you wish to be in protecting these codes is up to you.

Mnemonic recovery phrase booklet

Mnemonic recovery phrase booklet

There’s another option that could make you the envy of your friends. That’s the CryptoSteel wallet, which is a stainless steel metal case that comes with more than 250 stainless steel letter tiles engraved on each side. Codes and passwords are assembled manually from the supplied part-randomized set of tiles. Users are able to store up to 96 characters worth of confidential information. Cryptosteel claims to be fireproof, waterproof, and shock-proof.

image of a Cryptosteel cold storage device

Cryptosteel cold wallet

Of course, if you leave your Cryptosteel wallet in the pocket of a pair of ripped jeans that gets thrown out by the housekeeper, as happened to the character Russ Hanneman on the TV show Silicon Valley in last Sunday’s episode, then you’re out of luck. That fictional billionaire investor lost a USB drive with $300 million in cryptocoins. Let’s hope that doesn’t happen to you.

Encryption & Security

Whether you store your keys on your computer, an external disk, a USB drive, DAS, NAS, or in the cloud, you want to make sure that no one else can use those keys. The best way to handle that is to encrypt the backup.

With Backblaze Backup for Windows and Macintosh, your backups are encrypted in transmission to the cloud and on the backup server. Users have the option to add an additional level of security by adding a Personal Encryption Key (PEK), which secures their private key. Your cryptocurrency backup files are secure in the cloud. Using our web or mobile interface, previous versions of files can be accessed, as well.

Our object storage cloud offering, Backblaze B2, can be used with a variety of applications for Windows, Macintosh, and Linux. With B2, cryptocurrency users can choose whichever method of encryption they wish to use on their local computers and then upload their encrypted currency keys to the cloud. Depending on the client used, versioning and life-cycle rules can be applied to the stored files.

Other backup programs and systems provide some or all of these capabilities, as well. If you are backing up to a local drive, it is a good idea to encrypt the local backup, which is an option in some backup programs.

Address Security

Some experts recommend using a different address for each cryptocurrency transaction. Since the address is not the same as your wallet, this means that you are not creating a new wallet, but simply using a new identifier for people sending you cryptocurrency. Creating a new address is usually as easy as clicking a button in the wallet.

One of the chief advantages of using a different address for each transaction is anonymity. Each time you use an address, you put more information into the public ledger (blockchain) about where the currency came from or where it went. That means that over time, using the same address repeatedly could mean that someone could map your relationships, transactions, and incoming funds. The more you use that address, the more information someone can learn about you. For more on this topic, refer to Address reuse.

Note that a downside of using a paper wallet with a single key pair (type-0 non-deterministic wallet) is that it has the vulnerabilities listed above. Each transaction using that paper wallet will add to the public record of transactions associated with that address. Newer wallets, i.e. “deterministic” or those using mnemonic code words support multiple addresses and are now recommended.

There are other approaches to keeping your cryptocurrency transaction secure. Here are a couple of them.

Multi-signature

Multi-signature refers to requiring more than one key to authorize a transaction, much like requiring more than one key to open a safe. It is generally used to divide up responsibility for possession of cryptocurrency. Standard transactions could be called “single-signature transactions” because transfers require only one signature — from the owner of the private key associated with the currency address (public key). Some wallets and apps can be configured to require more than one signature, which means that a group of people, businesses, or other entities all must agree to trade in the cryptocurrencies.

Deep Cold Storage

Deep cold storage ensures the entire transaction process happens in an offline environment. There are typically three elements to deep cold storage.

First, the wallet and private key are generated offline, and the signing of transactions happens on a system not connected to the internet in any manner. This ensures it’s never exposed to a potentially compromised system or connection.

Second, details are secured with encryption to ensure that even if the wallet file ends up in the wrong hands, the information is protected.

Third, storage of the encrypted wallet file or paper wallet is generally at a location or facility that has restricted access, such as a safety deposit box at a bank.

Deep cold storage is used to safeguard a large individual cryptocurrency portfolio held for the long term, or for trustees holding cryptocurrency on behalf of others, and is possibly the safest method to ensure a crypto investment remains secure.

Keep Your Software Up to Date

You should always make sure that you are using the latest version of your app or wallet software, which includes important stability and security fixes. Installing updates for all other software on your computer or mobile device is also important to keep your wallet environment safer.

One Last Thing: Think About Your Testament

Your cryptocurrency funds can be lost forever if you don’t have a backup plan for your peers and family. If the location of your wallets or your passwords is not known by anyone when you are gone, there is no hope that your funds will ever be recovered. Taking a bit of time on these matters can make a huge difference.

To the Moon*

Are you comfortable with how you’re managing and backing up your cryptocurrency wallets and keys? Do you have a suggestion for keeping your cryptocurrencies safe that we missed above? Please let us know in the comments.

*To the Moon — Crypto slang for a currency that reaches an optimistic price projection.

The post Securing Your Cryptocurrency appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Bad Software Is Our Fault

Post Syndicated from Bozho original https://techblog.bozho.net/bad-software-is-our-fault/

Bad software is everywhere. One can even claim that every software is bad. Cool companies, tech giants, established companies, all produce bad software. And no, yours is not an exception.

Who’s to blame for bad software? It’s all complicated and many factors are intertwined – there’s business requirements, there’s organizational context, there’s lack of sufficient skilled developers, there’s the inherent complexity of software development, there’s leaky abstractions, reliance on 3rd party software, consequences of wrong business and purchase decisions, time limitations, flawed business analysis, etc. So yes, despite the catchy title, I’m aware it’s actually complicated.

But in every “it’s complicated” scenario, there’s always one or two factors that are decisive. All of them contribute somehow, but the major drivers are usually a handful of things. And in the case of base software, I think it’s the fault of technical people. Developers, architects, ops.

We don’t seem to care about best practices. And I’ll do some nasty generalizations here, but bear with me. We can spend hours arguing about tabs vs spaces, curly bracket on new line, git merge vs rebase, which IDE is better, which framework is better and other largely irrelevant stuff. But we tend to ignore the important aspects that span beyond the code itself. The context in which the code lives, the non-functional requirements – robustness, security, resilience, etc.

We don’t seem to get security. Even trivial stuff such as user authentication is almost always implemented wrong. These days Twitter and GitHub realized they have been logging plain-text passwords, for example, but that’s just the tip of the iceberg. Too often we ignore the security implications.

“But the business didn’t request the security features”, one may say. The business never requested 2-factor authentication, encryption at rest, PKI, secure (or any) audit trail, log masking, crypto shredding, etc., etc. Because the business doesn’t know these things – we do and we have to put them on the backlog and fight for them to be implemented. Each organization has its specifics and tech people can influence the backlog in different ways, but almost everywhere we can put things there and prioritize them.

The other aspect is testing. We should all be well aware by now that automated testing is mandatory. We have all the tools in the world for unit, functional, integration, performance and whatnot testing, and yet many software projects lack the necessary test coverage to be able to change stuff without accidentally breaking things. “But testing takes time, we don’t have it”. We are perfectly aware that testing saves time, as we’ve all had those “not again!” recurring bugs. And yet we think of all sorts of excuses – “let the QAs test it”, we have to ship that now, we’ll test it later”, “this is too trivial to be tested”, etc.

And you may say it’s not our job. We don’t define what has do be done, we just do it. We don’t define the budget, the scope, the features. We just write whatever has been decided. And that’s plain wrong. It’s not our job to make money out of our code, and it’s not our job to define what customers need, but apart from that everything is our job. The way the software is structured, the security aspects and security features, the stability of the code base, the way the software behaves in different environments. The non-functional requirements are our job, and putting them on the backlog is our job.

You’ve probably heard that every software becomes “legacy” after 6 months. And that’s because of us, our sloppiness, our inability to mitigate external factors and constraints. Too often we create a mess through “just doing our job”.

And of course that’s a generalization. I happen to know a lot of great professionals who don’t make these mistakes, who strive for excellence and implement things the right way. But our industry as a whole doesn’t. Our industry as a whole produces bad software. And it’s our fault, as developers – as the only people who know why a certain piece of software is bad.

In a talk of his, Bob Martin warns us of the risks of our sloppiness. We have been building websites so far, but we are more and more building stuff that interacts with the real world, directly and indirectly. Ultimately, lives may depend on our software (like the recent unfortunate death caused by a self-driving car). And I’ll agree with Uncle Bob that it’s high time we self-regulate as an industry, before some technically incompetent politician decides to do that.

How, I don’t know. We’ll have to think more about it. But I’m pretty sure it’s our fault that software is bad, and no amount of blaming the management, the budget, the timing, the tools or the process can eliminate our responsibility.

Why do I insist on bashing my fellow software engineers? Because if we start looking at software development with more responsibility; with the fact that if it fails, it’s our fault, then we’re more likely to get out of our current bug-ridden, security-flawed, fragile software hole and really become the experts of the future.

The post Bad Software Is Our Fault appeared first on Bozho's tech blog.

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

Post Syndicated from YongSeong Lee original https://aws.amazon.com/blogs/big-data/analyze-data-in-amazon-dynamodb-using-amazon-sagemaker-for-real-time-prediction/

Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.

Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.

DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.

To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.

The solution that I describe provides the following benefits:

  • Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
  • Automatically updates your model to get real-time predictions
  • Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
  • Makes it easier for developers of all skill levels to use Amazon SageMaker

All code and data set in this post are available in this .zip file.

Solution architecture

The following diagram shows the overall architecture of the solution.

The steps that data follows through the architecture are as follows:

  1. Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
  2. Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
  3. Amazon SageMaker renews the model artifact and update the endpoint.
  4. The converted CSV is available for ad hoc queries with Amazon Athena.
  5. Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.

Building the auto-updating model

This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.

Download sample scripts and data

Before you begin, take the following steps:

  1. Download sample scripts in this .zip file.
  2. Unzip the src.zip file.
  3. Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
  4. Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.

For this solution, the banking.csv  should be imported into a DynamoDB table.

Export a DynamoDB table

To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.

One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.

For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.

Add the script to an existing pipeline

After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:

  1. Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
  2. For Actions, choose Edit.
  3. In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.

Paste the following command into the new step after the data ­­upload step:

s3://#{myDDBRegion}.elasticmapreduce/libs/script-runner/script-runner.jar,s3://<your bucket name>/automation_script.sh,#{output.directoryPath},#{myDDBRegion}

The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.

The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.

Automation script: Convert JSON data to CSV with Hive

We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.

When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.

Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.

The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.

#!/bin/bash
hive -e "
ADD jar s3://<your bucket name>/json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar ; 
DROP TABLE IF EXISTS blog_backup_data ;
CREATE EXTERNAL TABLE blog_backup_data (
 customer_id map<string,string>,
 age map<string,string>, job map<string,string>, 
 marital map<string,string>,education map<string,string>, 
 default map<string,string>, housing map<string,string>,
 loan map<string,string>, contact map<string,string>, 
 month map<string,string>, day_of_week map<string,string>, 
 duration map<string,string>, campaign map<string,string>,
 pdays map<string,string>, previous map<string,string>, 
 poutcome map<string,string>, emp_var_rate map<string,string>, 
 cons_price_idx map<string,string>, cons_conf_idx map<string,string>,
 euribor3m map<string,string>, nr_employed map<string,string>, 
 y map<string,string> ) 
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' 
LOCATION '$1/';

INSERT OVERWRITE DIRECTORY 's3://<your bucket name>/<datasource path>/' 
SELECT concat( customer_id['s'],',', 
 age['n'],',', job['s'],',', 
 marital['s'],',', education['s'],',', default['s'],',', 
 housing['s'],',', loan['s'],',', contact['s'],',', 
 month['s'],',', day_of_week['s'],',', duration['n'],',', 
 campaign['n'],',',pdays['n'],',',previous['n'],',', 
 poutcome['s'],',', emp_var_rate['n'],',', cons_price_idx['n'],',',
 cons_conf_idx['n'],',', euribor3m['n'],',', nr_employed['n'],',', y['n'] ) 
FROM blog_backup_data
WHERE customer_id['s'] > 0 ;

After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.

Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id  columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.

Automation script: Renew the Amazon SageMaker model

After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3.  For renewing model artifact, you must create a new training job.  Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.

In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.

#!/bin/bash
## Define variable 
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>" 


# Select containers image based on region.  
case "$REGION" in
"us-west-2" )
    IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
    ;;
"us-east-1" )
    IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest" 
    ;;
"us-east-2" )
    IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest" 
    ;;
"eu-west-1" )
    IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest" 
    ;;
 *)
    echo "Invalid Region Name"
    exit 1 ;  
esac

# Start training job and creating model artifact 
TRAINING_JOB_NAME=TRAIN-${DTTIME} 
S3OUTPUT="s3://<your bucket name>/model/" 
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5 
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE}  --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None"  }]'  --output-data-config S3OutputPath=${S3OUTPUT} --resource-config  InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier  

# Wait until job completed 
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME}  --region ${REGION}

# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME}  --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT}  --execution-role-arn ${ROLE}

# create a new endpoint configuration 
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker  create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME}  --production-variants  VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge

# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name  ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
    aws sagemaker  create-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}    
else
    aws sagemaker  update-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi

Grant permission

Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": [
 "sagemaker:CreateTrainingJob",
 "sagemaker:DescribeTrainingJob",
 "sagemaker:CreateModel",
 "sagemaker:CreateEndpointConfig",
 "sagemaker:DescribeEndpoint",
 "sagemaker:CreateEndpoint",
 "sagemaker:UpdateEndpoint",
 "iam:PassRole"
 ],
 "Resource": "*"
 }
 ]
}

Use real-time prediction

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.

Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.

=== Python sample for real-time prediction ===

#!/usr/bin/env python
import boto3
import json 

client = boto3.client('sagemaker-runtime', region_name ='<your region>' )
new_customer_info = '34,10,2,4,1,2,1,1,6,3,190,1,3,4,3,-1.7,94.055,-39.8,0.715,4991.6'
response = client.invoke_endpoint(
    EndpointName='ServiceEndpoint',
    Body=new_customer_info, 
    ContentType='text/csv'
)
result = json.loads(response['Body'].read().decode())
print(result)
--- output(response) ---
{u'predictions': [{u'score': 0.7528127431869507, u'predicted_label': 1.0}]}

Solution summary

The solution takes the following steps:

  1. Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
  2. Train the Amazon SageMaker model with the new data source.
  3. When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
  4. If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.

Running ad hoc queries using Amazon Athena

Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.

With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog. 

Creating an Amazon Athena table and running it

Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.

=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
 age int, 
 job string, 
 marital string , 
 education string, 
 default string, 
 housing string, 
 loan string, 
 contact string, 
 month string, 
 day_of_week string, 
 duration int, 
 campaign int, 
 pdays int , 
 previous int , 
 poutcome string, 
 emp_var_rate double, 
 cons_price_idx double,
 cons_conf_idx double, 
 euribor3m double, 
 nr_employed double, 
 y int 
)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n' 
LOCATION 's3://<your bucket name>/<datasource path>/';

The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.

=== Sample Query ===

SELECT corr(age,y) AS correlation_age_and_target, 
 corr(duration,y) AS correlation_duration_and_target, 
 corr(campaign,y) AS correlation_campaign_and_target,
 corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y , 
 CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact 
 FROM datasource 
 ) datasource ;

Conclusion

In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.

You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.

 

Additional Reading

If you found this post useful, be sure to check out Serving Real-Time Machine Learning Predictions on Amazon EMR and Analyzing Data in S3 using Amazon Athena.

 

About the Author

Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”

 

 

Improve SMS Message Delivery with Phone Number Verify in Amazon Pinpoint

Post Syndicated from Kadir Rathnavelu original https://aws.amazon.com/blogs/messaging-and-targeting/improve-sms-message-delivery-with-phone-number-verify/

SMS messaging is becoming an increasingly vital tool for companies in a variety of industries and across a diverse range of use cases. For example, many app developers use SMS messaging as part of their process for onboarding new customers. When new customers sign up for a service, they’re asked to provide their mobile phone numbers. The developer sends the customer a one-time password in an SMS message, which the customer then enters into a form on the web or in an app to complete the registration process. This capability is an important tool for ensuring the security of customers’ accounts. However, if the customer doesn’t receive the SMS message that contains the one-time password, he or she may become frustrated, and might abandon the registration process completely.

There are many things that could prevent an SMS message from arriving, but the most common causes are data entry errors. Apps and web forms typically only perform basic verification of phone numbers that end users provide. For example, they might check to make sure customers only entered numeric characters, or that the number contains the right number of digits. When customers enter their mobile numbers into an app or on a web form, they might accidentally enter their country code twice, or not enter a country code at all, or enter a leading zero in front of their phone number, or any of a variety of other common errors. In some cases, customers might even enter numbers that aren’t capable of receiving SMS messages, such as landline or Voice over IP (VoIP) numbers. Basic number validation techniques won’t stop any of these common errors from occurring. To help our customers find and correct these common issues, we’re launching a new feature in Amazon Pinpoint called Phone Number Verify.

With Phone Number Verify, Amazon Pinpoint checks to see whether the phone number provided is in a valid format based on its country code prefix. If the number is formatted correctly, Amazon Pinpoint leaves the number as the customer entered it. If the number isn’t formatted correctly, Amazon Pinpoint compares the phone number to various rules to make it valid. For example, if a customer enters a leading zero after the country code, Phone Number Verify removes the extra character to make the number valid. In this case, if the customer entered +1 0 206 555 0199 (a United States phone number in the 206 area code), Phone Number Verify changes the number to a correctly formatted number without the extra zero (+1 206 555 0199).

In addition to formatting numbers properly, Phone Number Verify can also detect and fix country-specific nuances. For example, in Brazil, older mobile phone numbers had eight digits. To create a larger pool of possible mobile phone numbers, Brazilian mobile companies added a ninth digit, and added a 9 to the beginning of the older eight digit numbers. If a customer provides a Brazilian mobile number that contains eight digits, Phone Number Verify will detect the issue and automatically insert the 9 in the appropriate place, between the area code and the phone number. In this example, Phone Number Verify would change +55 11 9123 4567 (a number in country code 55 and area code 11) to +55 11 99123 4567. By capturing and addressing these issues at the time of entry, Phone Number Verify can potentially increase the number of customers you’re able to contact. Our internal testing shows that up to 10% of mobile phone numbers contain these kinds of errors—that’s 10% of your potential customers that you might not be able to contact otherwise!

Phone Number Verify also returns metadata about phone numbers, such as the name of the telephone carrier, the type of phone number (landline, VoIP, mobile), and the geographic location (city, country, state and time zone) where the number is registered. Amazon Pinpoint users can use this metadata to perform additional validation. For example, if a user provides a landline number, you can immediately prompt the customer to enter a phone number that is capable of receiving text messages. Alternatively, when a customer provides a landline or VoIP number, you can call the user by using text-to-speech technology, rather than by sending a text message.

Phone Number Verify is now available in Amazon Pinpoint in a limited release. If you’re interested in testing this feature, complete our application form.

OMG The Stupid It Burns

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/04/omg-stupid-it-burns.html

This article, pointed out by @TheGrugq, is stupid enough that it’s worth rebutting.

The article starts with the question “Why did the lessons of Stuxnet, Wannacry, Heartbleed and Shamoon go unheeded?“. It then proceeds to ignore the lessons of those things.
Some of the actual lessons should be things like how Stuxnet crossed air gaps, how Wannacry spread through flat Windows networking, how Heartbleed comes from technical debt, and how Shamoon furthers state aims by causing damage.
But this article doesn’t cover the technical lessons. Instead, it thinks the lesson should be the moral lesson, that we should take these things more seriously. But that’s stupid. It’s the sort of lesson people teach you that know nothing about the topic. When you have nothing of value to contribute to a topic you can always take the moral high road and criticize everyone for being morally weak for not taking it more seriously. Obviously, since doctors haven’t cured cancer yet, it’s because they don’t take the problem seriously.
The article continues to ignore the lesson of these cyber attacks and instead regales us with a list of military lessons from WW I and WW II. This makes the same flaw that many in the military make, trying to understand cyber through analogies with the real world. It’s not that such lessons could have no value, it’s that this article contains a poor list of them. It seems to consist of a random list of events that appeal to the author rather than events that have bearing on cybersecurity.
Then, in case we don’t get the point, the article bullies us with hyperbole, cliches, buzzwords, bombastic language, famous quotes, and citations. It’s hard to see how most of them actually apply to the text. Rather, it seems like they are included simply because he really really likes them.
The article invests much effort in discussing the buzzword “OODA loop”. Most attacks in cyberspace don’t have one. Instead, attackers flail around, trying lots of random things, overcoming defense with brute-force rather than an understanding of what’s going on. That’s obviously the case with Wannacry: it was an accident, with the perpetrator experimenting with what would happen if they added the ETERNALBLUE exploit to their existing ransomware code. The consequence was beyond anybody’s ability to predict.
You might claim that this is just the first stage, that they’ll loop around, observe Wannacry’s effects, orient themselves, decide, then act upon what they learned. Nope. Wannacry burned the exploit. It’s essentially removed any vulnerable systems from the public Internet, thereby making it impossible to use what they learned. It’s still active a year later, with infected systems behind firewalls busily scanning the Internet so that if you put a new system online that’s vulnerable, it’ll be taken offline within a few hours, before any other evildoer can take advantage of it.
See what I’m doing here? Learning the actual lessons of things like Wannacry? The thing the above article fails to do??
The article has a humorous paragraph on “defense in depth”, misunderstanding the term. To be fair, it’s the cybersecurity industry’s fault: they adopted then redefined the term. That’s why there’s two separate articles on Wikipedia: one for the old military term (as used in this article) and one for the new cybersecurity term.
As used in the cybersecurity industry, “defense in depth” means having multiple layers of security. Many organizations put all their defensive efforts on the perimeter, and none inside a network. The idea of “defense in depth” is to put more defenses inside the network. For example, instead of just one firewall at the edge of the network, put firewalls inside the network to segment different subnetworks from each other, so that a ransomware infection in the customer support computers doesn’t spread to sales and marketing computers.
The article talks about exploiting WiFi chips to bypass the defense in depth measures like browser sandboxes. This is conflating different types of attacks. A WiFi attack is usually considered a local attack, from somebody next to you in bar, rather than a remote attack from a server in Russia. Moreover, far from disproving “defense in depth” such WiFi attacks highlight the need for it. Namely, phones need to be designed so that successful exploitation of other microprocessors (namely, the WiFi, Bluetooth, and cellular baseband chips) can’t directly compromise the host system. In other words, once exploited with “Broadpwn”, a hacker would need to extend the exploit chain with another vulnerability in the hosts Broadcom WiFi driver rather than immediately exploiting a DMA attack across PCIe. This suggests that if PCIe is used to interface to peripherals in the phone that an IOMMU be used, for “defense in depth”.
Cybersecurity is a young field. There are lots of useful things that outsider non-techies can teach us. Lessons from military history would be well-received.
But that’s not this story. Instead, this story is by an outsider telling us we don’t know what we are doing, that they do, and then proceeds to prove they don’t know what they are doing. Their argument is based on a moral suasion and bullying us with what appears on the surface to be intellectual rigor, but which is in fact devoid of anything smart.
My fear, here, is that I’m going to be in a meeting where somebody has read this pretentious garbage, explaining to me why “defense in depth” is wrong and how we need to OODA faster. I’d rather nip this in the bud, pointing out if you found anything interesting from that article, you are wrong.

Securing Elections

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/securing_electi_1.html

Elections serve two purposes. The first, and obvious, purpose is to accurately choose the winner. But the second is equally important: to convince the loser. To the extent that an election system is not transparently and auditably accurate, it fails in that second purpose. Our election systems are failing, and we need to fix them.

Today, we conduct our elections on computers. Our registration lists are in computer databases. We vote on computerized voting machines. And our tabulation and reporting is done on computers. We do this for a lot of good reasons, but a side effect is that elections now have all the insecurities inherent in computers. The only way to reliably protect elections from both malice and accident is to use something that is not hackable or unreliable at scale; the best way to do that is to back up as much of the system as possible with paper.

Recently, there have been two graphic demonstrations of how bad our computerized voting system is. In 2007, the states of California and Ohio conducted audits of their electronic voting machines. Expert review teams found exploitable vulnerabilities in almost every component they examined. The researchers were able to undetectably alter vote tallies, erase audit logs, and load malware on to the systems. Some of their attacks could be implemented by a single individual with no greater access than a normal poll worker; others could be done remotely.

Last year, the Defcon hackers’ conference sponsored a Voting Village. Organizers collected 25 pieces of voting equipment, including voting machines and electronic poll books. By the end of the weekend, conference attendees had found ways to compromise every piece of test equipment: to load malicious software, compromise vote tallies and audit logs, or cause equipment to fail.

It’s important to understand that these were not well-funded nation-state attackers. These were not even academics who had been studying the problem for weeks. These were bored hackers, with no experience with voting machines, playing around between parties one weekend.

It shouldn’t be any surprise that voting equipment, including voting machines, voter registration databases, and vote tabulation systems, are that hackable. They’re computers — often ancient computers running operating systems no longer supported by the manufacturers — and they don’t have any magical security technology that the rest of the industry isn’t privy to. If anything, they’re less secure than the computers we generally use, because their manufacturers hide any flaws behind the proprietary nature of their equipment.

We’re not just worried about altering the vote. Sometimes causing widespread failures, or even just sowing mistrust in the system, is enough. And an election whose results are not trusted or believed is a failed election.

Voting systems have another requirement that makes security even harder to achieve: the requirement for a secret ballot. Because we have to securely separate the election-roll system that determines who can vote from the system that collects and tabulates the votes, we can’t use the security systems available to banking and other high-value applications.

We can securely bank online, but can’t securely vote online. If we could do away with anonymity — if everyone could check that their vote was counted correctly — then it would be easy to secure the vote. But that would lead to other problems. Before the US had the secret ballot, voter coercion and vote-buying were widespread.

We can’t, so we need to accept that our voting systems are insecure. We need an election system that is resilient to the threats. And for many parts of the system, that means paper.

Let’s start with the voter rolls. We know they’ve already been targeted. In 2016, someone changed the party affiliation of hundreds of voters before the Republican primary. That’s just one possibility. A well-executed attack that deletes, for example, one in five voters at random — or changes their addresses — would cause chaos on election day.

Yes, we need to shore up the security of these systems. We need better computer, network, and database security for the various state voter organizations. We also need to better secure the voter registration websites, with better design and better internet security. We need better security for the companies that build and sell all this equipment.

Multiple, unchangeable backups are essential. A record of every addition, deletion, and change needs to be stored on a separate system, on write-only media like a DVD. Copies of that DVD, or — even better — a paper printout of the voter rolls, should be available at every polling place on election day. We need to be ready for anything.

Next, the voting machines themselves. Security researchers agree that the gold standard is a voter-verified paper ballot. The easiest (and cheapest) way to achieve this is through optical-scan voting. Voters mark paper ballots by hand; they are fed into a machine and counted automatically. That paper ballot is saved, and serves as a final true record in a recount in case of problems. Touch-screen machines that print a paper ballot to drop in a ballot box can also work for voters with disabilities, as long as the ballot can be easily read and verified by the voter.

Finally, the tabulation and reporting systems. Here again we need more security in the process, but we must always use those paper ballots as checks on the computers. A manual, post-election, risk-limiting audit varies the number of ballots examined according to the margin of victory. Conducting this audit after every election, before the results are certified, gives us confidence that the election outcome is correct, even if the voting machines and tabulation computers have been tampered with. Additionally, we need better coordination and communications when incidents occur.

It’s vital to agree on these procedures and policies before an election. Before the fact, when anyone can win and no one knows whose votes might be changed, it’s easy to agree on strong security. But after the vote, someone is the presumptive winner — and then everything changes. Half of the country wants the result to stand, and half wants it reversed. At that point, it’s too late to agree on anything.

The politicians running in the election shouldn’t have to argue their challenges in court. Getting elections right is in the interest of all citizens. Many countries have independent election commissions that are charged with conducting elections and ensuring their security. We don’t do that in the US.

Instead, we have representatives from each of our two parties in the room, keeping an eye on each other. That provided acceptable security against 20th-century threats, but is totally inadequate to secure our elections in the 21st century. And the belief that the diversity of voting systems in the US provides a measure of security is a dangerous myth, because few districts can be decisive and there are so few voting-machine vendors.

We can do better. In 2017, the Department of Homeland Security declared elections to be critical infrastructure, allowing the department to focus on securing them. On 23 March, Congress allocated $380m to states to upgrade election security.

These are good starts, but don’t go nearly far enough. The constitution delegates elections to the states but allows Congress to “make or alter such Regulations”. In 1845, Congress set a nationwide election day. Today, we need it to set uniform and strict election standards.

This essay originally appeared in the Guardian.

My letter urging Georgia governor to veto anti-hacking bill

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/04/my-letter-urging-georgia-governor-to.html

April 16, 2018

Office of the Governor
206 Washington Street
111 State Capitol
Atlanta, Georgia 30334

Re: SB 315

Dear Governor Deal:

I am writing to urge you to veto SB315, the “Unauthorized Computer Access” bill.

The cybersecurity community, of which Georgia is a leader, is nearly unanimous that SB315 will make cybersecurity worse. You’ve undoubtedly heard from many of us opposing this bill. It does not help in prosecuting foreign hackers who target Georgian computers, such as our elections systems. Instead, it prevents those who notice security flaws from pointing them out, thereby getting them fixed. This law violates the well-known Kirchhoff’s Principle, that instead of secrecy and obscurity, that security is achieved through transparency and openness.

That the bill contains this flaw is no accident. The justification for this bill comes from an incident where a security researcher noticed a Georgia state election system had made voter information public. This remained unfixed, months after the vulnerability was first disclosed, leaving the data exposed. Those in charge decided that it was better to prosecute those responsible for discovering the flaw rather than punish those who failed to secure Georgia voter information, hence this law.

Too many security experts oppose this bill for it to go forward. Signing this bill, one that is weak on cybersecurity by favoring political cover-up over the consensus of the cybersecurity community, will be part of your legacy. I urge you instead to veto this bill, commanding the legislature to write a better one, this time consulting experts, which due to Georgia’s thriving cybersecurity community, we do not lack.

Thank you for your attention.

Sincerely,
Robert Graham
(formerly) Chief Scientist, Internet Security Systems

Artefacts in the classroom with Museum in a Box

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/museum-in-a-box/

Museum in a Box bridges the gap between museums and schools by creating a more hands-on approach to conservation education through 3D printing and digital making.

Artefacts in the classroom with Museum in a Box || Raspberry Pi Stories

Learn more: http://rpf.io/ Subscribe to our YouTube channel: http://rpf.io/ytsub Help us reach a wider audience by translating our video content: http://rpf.io/yttranslate Buy a Raspberry Pi from one of our Approved Resellers: http://rpf.io/ytproducts Find out more about the Raspberry Pi Foundation: Raspberry Pi http://rpf.io/ytrpi Code Club UK http://rpf.io/ytccuk Code Club International http://rpf.io/ytcci CoderDojo http://rpf.io/ytcd Check out our free online training courses: http://rpf.io/ytfl Find your local Raspberry Jam event: http://rpf.io/ytjam Work through our free online projects: http://rpf.io/ytprojects Do you have a question about your Raspberry Pi?

Fantastic collections and where to find them

Large, impressive statues are truly a sight to be seen. Take for example the 2.4m Hoa Hakananai’a at the British Museum. Its tall stature looms over you as you read its plaque to learn of the statue’s journey from Easter Island to the UK under the care of Captain Cook in 1774, and you can’t help but wonder at how it made it here in one piece.

Hoa Hakananai’a Captain Cook British Museum
Hoa Hakananai’a Captain Cook British Museum

But unless you live near a big city where museums are plentiful, you’re unlikely to see the likes of Hoa Hakananai’a in person. Instead, you have to content yourself with online photos or videos of world-famous artefacts.

And that only accounts for the objects that are on display: conservators estimate that only approximately 5 to 10% of museums’ overall collections are actually on show across the globe. The rest is boxed up in storage, inaccessible to the public due to risk of damage, or simply due to lack of space.

Museum in a Box

Museum in a Box aims to “put museum collections and expert knowledge into your hand, wherever you are in the world,” through modern maker practices such as 3D printing and digital making. With the help of the ‘Scan the World’ movement, an “ambitious initiative whose mission is to archive objects of cultural significance using 3D scanning technologies”, the Museum in a Box team has been able to print small, handheld replicas of some of the world’s most recognisable statues and sculptures.

Museum in a Box Raspberry Pi

Each 3D print gets NFC tags so it can initiate audio playback from a Raspberry Pi that sits snugly within the laser-cut housing of a ‘brain box’. Thus the print can talk directly to us through the magic of wireless technology, replacing the dense, dry text of a museum plaque with engaging speech.

Museum in a Box Raspberry Pi

The Museum in a Box team headed by CEO George Oates (featured in the video above) makes use of these 3D-printed figures alongside original artefacts, postcards, and more to bridge the gap between large, crowded, distant museums and local schools. Modeled after the museum handling collections that used to be sent to schools, Museum in a Box is a cheaper, more accessible alternative. Moreover, it not only allows for hands-on learning, but also encourages children to get directly involved by hacking its technology! With NFC technology readily available to the public, students can curate their own collections about their local area, record their own messages, and send their own box-sized museums on to schools in other towns or countries. In this way, Museum in a Box enables students to explore, and expand the reach of, their own histories.

Moving forward

With the technology perfected and interest in the project ever-growing, Museum in a Box has a busy year ahead. Supporting the new ‘Unstacked’ learning initiative, the team will soon be delivering ten boxes to the Smithsonian Libraries. The team has curated two collections specifically for this: an exploration into Asia-Pacific America experiences of migration to the USA throughout the 20th century, and a look into the history of science.

Smithsonian Library Museum in a Box Raspberry Pi

The team will also be making a box for the British Museum to support their Iraq Scheme initiative, and another box will be heading to the V&A to support their See Red programme. While primarily installed in the Lansbury Micro Museum, the box will also take to the road to visit the local Spotlight high school.

Museum in a Box at Raspberry Fields

Lastly, by far the most exciting thing the Museum in a Box team will be doing this year — in our opinion at least — is showcasing at Raspberry Fields! This is our brand-new festival of digital making that’s taking place on 30 June and 1 July 2018 here in Cambridge, UK. Find more information about it and get your ticket here.

The post Artefacts in the classroom with Museum in a Box appeared first on Raspberry Pi.

Safety first: a Raspberry Pi safety helmet

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/safety-helmet/

Jennifer Fox is back, this time with a Raspberry Pi Zero–controlled impact force monitor that will notify you if your collision is a worth a trip to the doctor.

Make an Impact Force Monitor!

Check out my latest Hacker in Residence project for SparkFun Electronics: the Helmet Guardian! It’s a Pi Zero powered impact force monitor that turns on an LED if your head/body experiences a potentially dangerous impact. Install in your sports helmets, bicycle, or car to keep track of impact and inform you when it’s time to visit the doctor.

Concussion

We’ve all knocked our heads at least once in our lives, maybe due to tripping over a loose paving slab, or to falling off a bike, or to walking into the corner of the overhead cupboard door for the third time this week — will I ever learn?! More often than not, even when we’re seeing stars, we brush off the accident and continue with our day, oblivious to the long-term damage we may be doing.

Force of impact

After some thorough research, Jennifer Fox, founder of FoxBot Industries, concluded that forces of 4 to 6 G sustained for more than a few seconds are dangerous to the human body. With this in mind, she decided to use a Raspberry Pi Zero W and an accelerometer to create helmet with an impact force monitor that notifies its wearer if this level of G-force has been met.

Jennifer Fox Raspberry Pi Impact Force Monitor

Obviously, if you do have a serious fall, you should always seek medical advice. This project is an example of how affordable technology can be used to create medical and citizen science builds, and not a replacement for professional medical services.

Setting up the impact monitor

Jennifer’s monitor requires only a few pieces of tech: a Zero W, an accelerometer and breakout board, a rechargeable USB battery, and an LED, plus the standard wires and resistors for these components.

After installing Raspbian, Jennifer enabled SSH and I2C on the Zero W to make it run headlessly, and then accessed it from a laptop. This allows her to control the Pi without physically connecting to it, and it makes for a wireless finished project.

Jen wired the Pi to the accelerometer breakout board and LED as shown in the schematic below.

Jennifer Fox Raspberry Pi Impact Force Monitor

The LED acts as a signal of significant impacts, turning on when the G-force threshold is reached, and not turning off again until the program is reset.

Jennifer Fox Raspberry Pi Impact Force Monitor

Make your own and more

Jennifer’s full code for the impact monitor is on GitHub, and she’s put together a complete tutorial on SparkFun’s website.

For more tutorials from Jennifer Fox, such as her ‘Bark Back’ IoT Pet Monitor, be sure to follow her on YouTube. And for similar projects, check out Matt’s smart bike light and Amelia Day’s physical therapy soccer ball.

The post Safety first: a Raspberry Pi safety helmet appeared first on Raspberry Pi.

New – Amazon DynamoDB Continuous Backups and Point-In-Time Recovery (PITR)

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/new-amazon-dynamodb-continuous-backups-and-point-in-time-recovery-pitr/

The Amazon DynamoDB team is back with another useful feature hot on the heels of encryption at rest. At AWS re:Invent 2017 we launched global tables and on-demand backup and restore of your DynamoDB tables and today we’re launching continuous backups with point-in-time recovery (PITR).

You can enable continuous backups with a single click in the AWS Management Console, a simple API call, or with the AWS Command Line Interface (CLI). DynamoDB can back up your data with per-second granularity and restore to any single second from the time PITR was enabled up to the prior 35 days. We built this feature to protect against accidental writes or deletes. If a developer runs a script against production instead of staging or if someone fat-fingers a DeleteItem call, PITR has you covered. We also built it for the scenarios you can’t normally predict. You can still keep your on-demand backups for as long as needed for archival purposes but PITR works as additional insurance against accidental loss of data. Let’s see how this works.

Continuous Backup

To enable this feature in the console we navigate to our table and select the Backups tab. From there simply click Enable to turn on the feature. I could also turn on continuous backups via the UpdateContinuousBackups API call.

After continuous backup is enabled we should be able to see an Earliest restore date and Latest restore date

Let’s imagine a scenario where I have a lot of old user profiles that I want to delete.

I really only want to send service updates to our active users based on their last_update date. I decided to write a quick Python script to delete all the users that haven’t used my service in a while.

import boto3
table = boto3.resource("dynamodb").Table("VerySuperImportantTable")
items = table.scan(
    FilterExpression="last_update >= :date",
    ExpressionAttributeValues={":date": "2014-01-01T00:00:00"},
    ProjectionExpression="ImportantId"
)['Items']
print("Deleting {} Items! Dangerous.".format(len(items)))
with table.batch_writer() as batch:
    for item in items:
        batch.delete_item(Key=item)

Great! This should delete all those pesky non-users of my service that haven’t logged in since 2013. So,— CTRL+C CTRL+C CTRL+C CTRL+C (interrupt the currently executing command).

Yikes! Do you see where I went wrong? I’ve just deleted my most important users! Oh, no! Where I had a greater-than sign, I meant to put a less-than! Quick, before Jeff Barr can see, I’m going to restore the table. (I probably could have prevented that typo with Boto 3’s handy DynamoDB conditions: Attr("last_update").lt("2014-01-01T00:00:00"))

Restoring

Luckily for me, restoring a table is easy. In the console I’ll navigate to the Backups tab for my table and click Restore to point-in-time.

I’ll specify the time (a few seconds before I started my deleting spree) and a name for the table I’m restoring to.

For a relatively small and evenly distributed table like mine, the restore is quite fast.

The time it takes to restore a table varies based on multiple factors and restore times are not neccesarily coordinated with the size of the table. If your dataset is evenly distributed across your primary keys you’ll be able to take advanatage of parallelization which will speed up your restores.

Learn More & Try It Yourself
There’s plenty more to learn about this new feature in the documentation here.

Pricing for continuous backups varies by region and is based on the current size of the table and all indexes.

A few things to note:

  • PITR works with encrypted tables.
  • If you disable PITR and later reenable it, you reset the start time from which you can recover.
  • Just like on-demand backups, there are no performance or availability impacts to enabling this feature.
  • Stream settings, Time To Live settings, PITR settings, tags, Amazon CloudWatch alarms, and auto scaling policies are not copied to the restored table.
  • Jeff, it turns out, knew I restored the table all along because every PITR API call is recorded in AWS CloudTrail.

Let us know how you’re going to use continuous backups and PITR on Twitter and in the comments.
– Randall

Simplicity is a Feature for Cloud Backup

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/distributed-cloud-backup-for-businesses/

cloud on a blue background
For Joel Wagener, Director of IT at AIBS, simplicity is an important feature he looks for in software applications to use in his organization. So maybe it’s not unexpected that Joel chose Backblaze for Business to back up AIBS’s staff computers. According to Joel, “It just works.”American Institute of Biological Sciences

AIBS (The American Institute of Biological Sciences) is a non-profit scientific association dedicated to advancing biological research and education. Founded in 1947 as part of the National Academy of Sciences, AIBS later became independent and now has over 100 member organizations. AIBS works to ensure that the public, legislators, funders, and the community of biologists have access to and use information that will guide them in making informed decisions about matters that require biological knowledge.

AIBS started using Backblaze for Business Cloud Backup several years ago to make sure that the organization’s data was backed up and protected from accidental loss or computer failure. AIBS is based in Washington, D.C., but is a virtual organization, with staff dispersed around the United States. AIBS needed a backup solution that worked anywhere a staff member was located, and was easy to use, as well. Joel has made Backblaze a default part of the configuration management for all the AIBS endpoints, which in their case are exclusively Macintosh.

AIBS biological images

“We started using Backblaze on a single computer in 2014, then not too long after that decided to deploy it to all our endpoints,” explains Joel. “We use Groups to oversee backups and for central billing, but we let each user manage their own computer and restore files on their own if they need to.”

“Backblaze stays out of the way until we need it. It’s fairly lightweight, and I appreciate that it’s simple,” says Joel. “It doesn’t throttle backups and the price point is good. I have family members who use Backblaze, as well.”

Backblaze’s Groups feature permits an organization to oversee and manage the user accounts, including restores, or let users handle that themselves. This flexibility fits a variety of organizations, where various degrees of oversight or independence are desirable. The finance and HR departments could manage their own data, for example, while the rest of the organization could be managed by IT. All groups can be billed centrally no matter how other functionality is set up.

“If we have a computer that needs repair, we can put a loaner computer in that person’s hands and they can immediately get the data they need directly from the Backblaze cloud backup, which is really helpful. When we get the original computer back from repair we can do a complete restore and return it to the user all ready to go again. When we’ve needed restores, Backblaze has been reliable.”

Joel also likes that the memory footprint of Backblaze is light — the clients for both Macintosh and Windows are native, and designed to use minimum system resources and not impact any applications used on the computer. He also likes that updates to the client software are pushed out when necessary.

Backblaze for Business

Backblaze for Business also helps IT maintain archives of users’ computers after they leave the organization.

“We like that we have a ready-made archive of a computer when someone leaves,” said Joel. The Backblaze backup is there if we need to retrieve anything that person was working on.”

There are other capabilities in Backblaze that Joel likes, but hasn’t had a chance to use yet.

“We’ve used Casper (Jamf) to deploy and manage software on endpoints without needing any interaction from the user. We haven’t used it yet for Backblaze, but we know that Backblaze supports it. It’s a handy feature to have.”

”It just works.”
— Joel Wagener, AIBS Director of IT

Perhaps the best thing about Backblaze for Business isn’t a specific feature that can be found on a product data sheet.

“When files have been lost, Backblaze has provided us access to multiple prior versions, and this feature has been important and worked well several times,” says Joel.

“That provides needed peace of mind to our users, and our IT department, as well.”

The post Simplicity is a Feature for Cloud Backup appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Power from wind: Open data on AWS

Post Syndicated from Caleb Phillips original https://aws.amazon.com/blogs/big-data/power-from-wind-open-data-on-aws/

Data that describe processes in a spatial context are everywhere in our day-to-day lives and they dominate big data problems. Map data, for instance, whether describing networks of roads or remote sensing data from satellites, get us where we need to go. Atmospheric data from simulations and sensors underlie our weather forecasts and climate models. Devices and sensors with GPS can provide a spatial context to nearly all mobile data.

In this post, we introduce the WIND toolkit, a huge (500 TB), open weather model dataset that’s available to the world on Amazon’s cloud services. We walk through how to access this data and some of the open-source software developed to make it easily accessible. Our solution considers a subset of geospatial data that exist on a grid (raster) and explores ways to provide access to large-scale raster data from weather models. The solution uses foundational AWS services and the Hierarchical Data Format (HDF), a well adopted format for scientific data.

The approach developed here can be extended to any data that fit in an HDF5 file, which can describe sparse and dense vectors and matrices of arbitrary dimensions. This format is already popular within the physical sciences for both experimental and simulation data. We discuss solutions to gridded data storage for a massive dataset of public weather model outputs called the Wind Integration National Dataset (WIND) toolkit. We also highlight strategies that are general to other large geospatial data management problems.

Wind Integration National Dataset

As variable renewable power penetration levels increase in power systems worldwide, the importance of renewable integration studies to ensure continued economic and reliable operation of the power grid is also increasing. The WIND toolkit is the largest freely available grid integration dataset to date.

The WIND toolkit was developed by 3TIER by Vaisala. They were under a subcontract to the National Renewable Energy Laboratory (NREL) to support studies on integration of wind energy into the existing US grid. NREL is a part of a network of national laboratories for the US Department of Energy and has a mission to advance the science and engineering of energy efficiency, sustainable transportation, and renewable power technologies.

The toolkit has been used by consultants, research groups, and universities worldwide to support grid integration studies. Less traditional uses also include resource assessments for wind plants (such as those powering Amazon data centers), and studying the effects of weather on California condor migrations in the Baja peninsula.

The diversity of applications highlights the value of accessible, open public data. Yet, there’s a catch: the dataset is huge. The WIND toolkit provides simulated atmospheric (weather) data at a two-km spatial resolution and five-minute temporal resolution at multiple heights for seven years. The entire dataset is half a petabyte (500 TB) in size and is stored in the NREL High Performance Computing data center in Golden, Colorado. Making this dataset publicly available easily and in a cost-effective manner is a major challenge.

As other laboratories and public institutions work to release their data to the world, they may face similar challenges to those that we experienced. Some prior, well-intentioned efforts to release huge datasets as-is have resulted in data resources that are technically available but fundamentally unusable. They may be stored in an unintuitive format or indexed and organized to support only a subset of potential uses. Downloading hundreds of terabytes of data is often impractical. Most users don’t have access to a big data cluster (or super computer) to slice and dice the data as they need after it’s downloaded.

We aim to provide a large amount of data (50 terabytes) to the public in a way that is efficient, scalable, and easy to use. In many cases, researchers can access these huge cloud-located datasets using the same software and algorithms they have developed for smaller datasets stored locally. Only the pieces of data they need for their individual analysis must be downloaded. To make this work in practice, we worked with the HDF Group and have built upon their forthcoming Highly Scalable Data Service.

In the rest of this post, we discuss how the HSDS software was developed to use Amazon EC2 and Amazon S3 resources to provide convenient and scalable access to these huge geospatial datasets. We describe how the HSDS service has been put to work for the WIND Toolkit dataset and demonstrate how to access it using the h5pyd Python library and the REST API. We conclude with information about our ongoing work to release more ‘open’ datasets to the public using AWS services, and ways to improve and extend the HSDS with newer Amazon services like Amazon ECS and AWS Lambda.

Developing a scalable service for big geospatial data

The HDF5 file format and API have been used for many years and is an effective means of storing large scientific datasets.  For example, NASA’s Earth Observing System (EOS) satellites collect more than 16 TBs of data per day using HDF5.

With the rise of the cloud, there are new challenges and opportunities to rethink how HDF5 can be enhanced to work effectively as a component in a cloud-native architecture.  For the HDF Group, working with NREL has been a great opportunity to put ideas into practice with a production-size dataset.

An HDF5 file consists of a directed graph of group and dataset objects.  Datasets can be thought of as a multidimensional array with support for user-defined metadata tags and compression.  Typical operations on datasets would be reading or writing data to a regular subregion (a hyperslab) or reading and writing individual elements (a point selection).   Also, group and dataset objects may each contain an arbitrary number of the user-defined metadata elements known as attributes.

Many people have used the HDF library in applications developed or ported to run on EC2 instances, but there are a number of constraints that often prove problematic:

  • The HDF5 library can’t read directly from HDF5 files stored as S3 objects. The entire file (often many GB in size) would need to be copied to local storage before the first byte can be read. Also, the instance must be configured with the appropriately sized EBS volume)
  • The HDF library only has access to the computational resources of the instance itself (as opposed to a cluster of instances), so many operations are bottlenecked by the library.
  • Any modifications to the HDF5 file would somehow have to be synchronized with changes that other instances have made to same file before writing back to S3.

Using a pattern common to many offerings from AWS, the solution to these constraints is to develop a service framework around the HDF data model. Using this model, the HDF Group has created the Highly Scalable Data Service (HSDS) that provides all the functionality that traditionally was provided by the HDF5 library.  By using the service, you don’t need to manage your own file volumes, but can just read and write whatever data that you need.

Because the service manages the actual data persistence to a durable medium (S3, in this case), you don’t need to worry about disk management. Simply stream the data you need from the service as you need it.  Secondly, putting the functionality behind a service allows some tricks to increase performance (described in more detail later). And lastly, HSDS allows any number of clients to access the data at the same time, enabling HDF5 to be used as a coordination mechanism for multiple readers and writers.

In designing the HSDS architecture, we gave much thought to how to achieve scalability of the HSDS service.  For accessing HDF5 data, there are two different types of scaling to consider:

  • Multiple clients making many requests to the service
  • Single requests that require a significant amount of data processing

To deal with the first scaling challenge, as with most services, we considered how the service responds as the request rate increases.  AWS provides some great tools that help in this regard:

  • Auto Scaling groups
  • Elastic Load Balancing load balancers
  • The ability of S3 to handle large aggregate throughput rates

By using a cluster of EC2 instances behind a load balancer, you can handle different client loads in a cost-effective manner.

The second scaling challenge concerns single requests that would take significant processing time with just one compute node.  One example of this from the WIND toolkit would be extracting all the values in the seven-year time span for a given geographic point and dataset.

In HDF5, large datasets are typically stored as “chunks”; that is, a regular partition of the array.  In HSDS, each chunk is stored as a binary object in S3.  The sequential approach to retrieving the time series values would be for the service to read each chunk needed from S3, extract the needed elements, and go on to the next chunk.  In this case, that would involve processing 2557 chunks, and would be quite slow.

Fortunately, with HSDS, you can speed this up quite a bit by exploiting the compute and I/O capabilities of the cluster.  Upon receiving the request, the receiving node can use other nodes in the cluster to read different portions of the selection.  With multiple nodes reading from S3 in parallel, performance improves as the cluster size increases.

The diagram below illustrates how this works in simplified case of four chunks and four nodes.

This architecture has worked in well in practice.  In testing with the WIND toolkit and time series extraction, we observed a request latency of ~60 seconds using four nodes vs. ~5 seconds with 40 nodes. Performance roughly scales with the size of the cluster.

A planned enhancement to this is to use AWS Lambda for the worker processing.  This enables 1000-way parallel reads at a reasonable cost, as you only pay for the milliseconds of CPU time used with AWS Lambda.

Public access to atmospheric data using HSDS and AWS

An early challenge in releasing the WIND toolkit data was in deciding how to subset the data for different use cases. In general, few researchers need access to the entire 0.5 PB of data and a great deal of efficiency and cost reduction can be gained by making directed constituent datasets.

NREL grid integration researchers initially extracted a 2-TB subset by selecting 120,000 points where the wind resource seemed appropriate for development. They also chose only those data important for wind applications (100-m wind speed, converted to power), the most interesting locations for those performing grid studies. To support the remaining users who needed more data resolution, we down-sampled the data to a 60-minute temporal resolution, keeping all the other variables and spatial resolution intact. This reduced dataset is 50 TB of data describing 30+ atmospheric variables of data for 7 years at a 60-minute temporal resolution.

The WindViz browser-based Gridded Wind Toolkit Visualizer was created as an example implementation of the HSDS REST API in JavaScript. The visualizer is written in the style of ECMAScript 2016 using a modern development toolchain that includes webpack and Babel. The source code is available through our GitHub repository. The demo page is hosted via GitHub pages, and we use a cross-origin AJAX request to fetch data from the HSDS service running on the EC2 infrastructure. The visualizer can be used to explore the gridded wind toolkit data on a map. Achieve full spatial resolution by zooming in to a specific region.

Programmatic access is possible using the h5pyd Python library, a distributed analog to the widely used h5py library. Users interact with the datasets (variables) and slice the data from its (time x longitude x latitude) cube form as they see fit.

Examples and use cases are described in a set of Jupyter notebooks and available on GitHub:

NREL/hsds-examples

To run these notebooks on an EC2 instance in the Oregon Region, run the following commands:

$ sudo yum install git gcc
$ sudo pip install –user jupyter
$ pip install --user git+http://github.com/HDFGroup/h5pyd.git
$ git clone https://github.com/NREL/hsds-examples.git
$ cd hsds-examples
$ jupyter notebook

Now you have a Jupyter notebook server running on your EC2 server.

From your laptop, create an SSH tunnel:

$ ssh –L 8888:localhost:8888 (IP address of the EC2 server)

Now, you can browse to localhost:8888 using the correct token, and interact with the notebooks as if they were local. Within the directory, there are examples for accessing the HSDS API and plotting wind and weather data using matplotlib.

Controlling access and defraying costs

A final concern is rate limiting and access control. Although the HSDS service is scalable and relatively robust, we had a few practical concerns:

  • How can we protect from malicious or accidental use that may lead to high egress fees (for example, someone who attempts to repeatedly download the entire dataset from S3)?
  • How can we keep track of who is using the data both to document the value of the data resource and to justify the costs?
  • If costs become too high, can we charge for some or all API use to help cover the costs?

To approach these problems, we investigated using Amazon API Gateway and its simplified integration with the AWS Marketplace for SaaS monetization as well as third-party API proxies.

In the end, we chose to use API Umbrella due to its close involvement with http://data.gov. While AWS Marketplace is a compelling option for future datasets, the decision was made to keep this dataset entirely open, at least for now. As community use and associated costs grow, we’ll likely revisit Marketplace. Meanwhile, API Umbrella provides controls for rate limiting and API key registration out of the box and was simple to implement as a front-end proxy to HSDS. Those applications that may want to charge for API use can accomplish a similar strategy using Amazon API Gateway and AWS Marketplace.

Ongoing work and other resources

As NREL and other government research labs, municipalities, and organizations try to share data with the public, we expect many of you will face similar challenges to those we have tried to approach with the architecture described in this post. Providing large datasets is one challenge. Doing so in a way that is affordable and convenient for users is an entirely more difficult goal. Using AWS cloud-native services and the existing foundation of the HDF file format has allowed us to tackle that challenge in a meaningful way.

 

Additional Reading

If you found this post useful, be sure to check out Perform Near Real-time Analytics on Streaming Data with Amazon Kinesis and Amazon Elasticsearch Service, Analyze OpenFDA Data in R with Amazon S3 and Amazon Athena and Querying OpenStreetMap with Amazon Athena.

 

About the Authors

Dr. Caleb Phillips is a senior scientist with the Data Analysis and Visualization Group within the Computational Sciences Center at the National Renewable Energy Laboratory. Caleb comes from a background in computer science systems, applied statistics, computational modeling, and optimization. His work at NREL spans the breadth of renewable energy technologies and focuses on applying modern data science techniques to data problems at scale.

 

 

 

Dr. Caroline Draxl is a senior scientist at NREL. She supports the research and modeling activities of the US Department of Energy from mesoscale to wind plant scale. Caroline uses mesoscale models to research wind resources in various countries, and participates in on- and offshore boundary layer research and in the coupling of the mesoscale flow features (kilometer scale) to the microscale (tens of meters). She holds a M.S. degree in Meteorology and Geophysics from the University of Innsbruck, Austria, and a PhD in Meteorology from the Technical University of Denmark.

 

 

 

John Readey has been a Senior Architect at The HDF Group since he joined in June 2014.  His interests include web services related to HDF, applications that support the use of HDF and data visualization.Before joining The HDF Group, John worked at Amazon.com from 2006–2014 where he developed service-based systems for eCommerce and AWS.

 

 

 

Jordan Perr-Sauer is an RPP intern with the Data Analysis and Visualization Group within the Computational Sciences Center at the National Renewable Energy Laboratory. Jordan hopes to use his professional background in software engineering and his academic training in applied mathematics to solve the challenging problems facing America and the world.

Setting up bug bounties for success

Post Syndicated from Michal Zalewski original https://lcamtuf.blogspot.com/2018/03/setting-up-bug-bounties-for-success.html

Bug bounties end up in the news with some regularity, usually for the wrong reasons. I’ve been itching to write
about that for a while – but instead of dwelling on the mistakes of the bygone days, I figured it may be better to
talk about some of the ways to get vulnerability rewards right.

What do you get out of bug bounties?

There’s plenty of differing views, but I like to think of such programs
simply as a bid on researchers’ time. In the most basic sense, you get three benefits:

  • Improved ability to detect bugs in production before they become major incidents.
  • A comparatively unbiased feedback loop to help you prioritize and measure other security work.
  • A robust talent pipeline for when you need to hire.

What bug bounties don’t offer?

You don’t get anything resembling a comprehensive security program or a systematic assessment of your platforms.
Researchers end up looking for bugs that offer favorable effort-to-payoff ratios for their skills and given the
very imperfect information they have about your enterprise. In other words, you may end up with a hundred
people looking for XSS and just one person looking for RCE.

Your reward structure can steer them toward the targets and bugs you care about, but it’s difficult to fully
eliminate this inherent skew. There’s only so far you can jack up your top-tier rewards, and only so far you can
go lowering the bottom-tier ones.

Don’t you have to outcompete the black market to get all the “good” bugs?

There is a free market price discovery component to it all: if you’re not getting the engagement you
were hoping for, you should probably consider paying more.

That said, there are going to be researchers who’d rather hurt you than work for you, no matter how much you pay;
you don’t have to win them over, and you don’t have to outspend every authoritarian government or
every crime syndicate. A bug bounty is effective simply if it attracts enough eyeballs to make bugs statistically
harder to find, and reduces the useful lifespan of any zero-days in black market trade. Plus, most
researchers don’t want their work to be used to crack down on dissidents in Egypt or Vietnam.

Another factor is that you’re paying for different things: a black market buyer probably wants a reliable exploit
capable of delivering payloads, and then demands silence for months or years to come; a vendor-run
bug bounty program is usually perfectly happy with a reproducible crash and doesn’t mind a researcher blogging
about their work.

In fact, while money is important, you will probably find out that it’s not enough to retain your top talent;
many folks want bug bounties to be more than a business transaction, and find a lot of value in having a close
relationship with your security team, comparing notes, and growing together. Fostering that partnership can
be more important than adding another $10,000 to your top reward.

How do I prevent it all from going horribly wrong?

Bug bounties are an unfamiliar beast to most lawyers and PR folks, so it’s a natural to be wary and try to plan
for every eventuality with pages and pages of impenetrable rules and fine-print legalese.

This is generally unnecessary: there is a strong self-selection bias, and almost every participant in a
vulnerability reward program will be coming to you in good faith. The more friendly, forthcoming, and
approachable you seem, and the more you treat them like peers, the more likely it is for your relationship to stay
positive. On the flip side, there is no faster way to make enemies than to make a security researcher feel that they
are now talking to a lawyer or to the PR dept.

Most people have strong opinions on disclosure policies; instead of imposing your own views, strive to patch reported bugs
reasonably quickly, and almost every reporter will play along. Demand researchers to cancel conference appearances,
take down blog posts, or sign NDAs, and you will sooner or later end up in the news.

But what if that’s not enough?

As with any business endeavor, mistakes will happen; total risk avoidance is seldom the answer. Learn to sincerely
apologize for mishaps; it’s not a sign of weakness to say “sorry, we messed up”. And you will almost certainly not end
up in the courtroom for doing so.

It’s good to foster a healthy and productive relationship with the community, so that they come to your defense when
something goes wrong. Encouraging people to disclose bugs and talk about their experiences is one way of accomplishing that.

What about extortion?

You should structure your program to naturally discourage bad behavior and make it stand out like a sore thumb.
Require bona fide reports with complete technical details before any reward decision is made by a panel of named peers;
and make it clear that you never demand non-disclosure as a condition of getting a reward.

To avoid researchers accidentally putting themselves in awkward situations, have clear rules around data exfiltration
and lateral movement: assure them that you will always pay based on the worst-case impact of their findings; in exchange,
ask them to stop as soon as they get a shell and never access any data that isn’t their own.

So… are there any downsides?

Yep. Other than souring up your relationship with the community if you implement your program wrong, the other consideration
is that bug bounties tend to generate a lot of noise from well-meaning but less-skilled researchers.

When this happens, do not get frustrated and do not penalize such participants; instead, help them grow. Consider
publishing educational articles, giving advice on how to investigate and structure reports, or
offering free workshops every now and then.

The other downside is cost; although bug bounties tend to offer far more bang for your buck than your average penetration
test, they are more random. The annual expenses tend to be fairly predictable, but there is always
some possibility of having to pay multiple top-tier rewards in rapid succession. This is the kind of uncertainty that
many mid-level budget planners react badly to.

Finally, you need to be able to fix the bugs you receive. It would be nuts to prefer to not know about the
vulnerabilities in the first place – but once you invite the research, the clock starts ticking and you need to
ship fixes reasonably fast.

So… should I try it?

There are folks who enthusiastically advocate for bug bounties in every conceivable situation, and people who dislike them
with fierce passion; both sentiments are usually strongly correlated with the line of business they are in.

In reality, bug bounties are not a cure-all, and there are some ways to make them ineffectual or even dangerous.
But they are not as risky or expensive as most people suspect, and when done right, they can actually be fun for your
team, too. You won’t know for sure until you try.