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Едно послание от Вашингтон в първия ден на предизборната кампания

Post Syndicated from nellyo original https://nellyo.wordpress.com/2021/03/05/%D0%B5%D0%B4%D0%BD%D0%BE-%D0%BF%D0%BE%D1%81%D0%BB%D0%B0%D0%BD%D0%B8%D0%B5-%D0%BE%D1%82-%D0%B2%D0%B0%D1%88%D0%B8%D0%BD%D0%B3%D1%82%D0%BE%D0%BD-%D0%B2-%D0%BF%D1%8A%D1%80%D0%B2%D0%B8%D1%8F-%D0%B4%D0%B5/

Съвместно двупартийно изявление на председателя на комисията по външна политика в Сената на САЩ Боб Менендес и най – старшия републиканец в комисията Джим Риш

“борбата с корупцията, възстановяването на независимите медии и насърчаването на върховенството на закона”

ГЕРБ избра тактиката на омаловажаване. Частно послание, неофициално, необвързващо.

No, RSA Is Not Broken

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/03/no-rsa-is-not-broken.html

I have been seeing this paper by cryptographer Peter Schnorr making the rounds: “Fast Factoring Integers by SVP Algorithms.” It describes a new factoring method, and its abstract ends with the provocative sentence: “This destroys the RSA cryptosystem.”

It does not. At best, it’s an improvement in factoring — and I’m not sure it’s even that. The paper is a preprint: it hasn’t been peer reviewed. Be careful taking its claims at face value.

Some discussion here.

I’ll append more analysis links to this post when I find them.

Chinese Hackers Stole an NSA Windows Exploit in 2014

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/03/chinese-hackers-stole-an-nsa-windows-exploit-in-2014.html

Check Point has evidence that (probably government affiliated) Chinese hackers stole and cloned an NSA Windows hacking tool years before (probably government affiliated) Russian hackers stole and then published the same tool. Here’s the timeline:

The timeline basically seems to be, according to Check Point:

  • 2013: NSA’s Equation Group developed a set of exploits including one called EpMe that elevates one’s privileges on a vulnerable Windows system to system-administrator level, granting full control. This allows someone with a foothold on a machine to commandeer the whole box.
  • 2014-2015: China’s hacking team code-named APT31, aka Zirconium, developed Jian by, one way or another, cloning EpMe.
  • Early 2017: The Equation Group’s tools were teased and then leaked online by a team calling itself the Shadow Brokers. Around that time, Microsoft cancelled its February Patch Tuesday, identified the vulnerability exploited by EpMe (CVE-2017-0005), and fixed it in a bumper March update. Interestingly enough, Lockheed Martin was credited as alerting Microsoft to the flaw, suggesting it was perhaps used against an American target.
  • Mid 2017: Microsoft quietly fixed the vulnerability exploited by the leaked EpMo exploit.

Lots of news articles about this.

Analyzing Freshdesk data using Amazon EventBridge and Amazon Athena

Post Syndicated from Benjamin Smith original https://aws.amazon.com/blogs/compute/analyzing-freshdesk-data-using-amazon-eventbridge-and-amazon-athena/

This post is written by Shashi Shankar, Application Architect, Shared Delivery Teams

Freshdesk is an omnichannel customer service platform by Freshworks. It provides automation services to help speed up customer support processes.

The Freshworks connector to Amazon EventBridge allows real time streaming of Freshdesk events with minimal configuration and setup. This integration provides real-time insights into customer support operations without the operational overhead of provisioning and maintaining any servers.

In this blog post, I walk through a serverless approach to ingest and analyze Freshdesk data. This solution uses EventBridge, Amazon Kinesis Data Firehose, Amazon S3, and Amazon Athena. I also look at examples of customer service questions that can be answered using this approach.

The following diagram shows a high-level architecture of the proposed solution:

  1. When a Freshdesk ticket is updated or created, the Freshworks connector pushes event data to the Amazon EventBridge partner event bus.
  2. A rule on the partner event bus pushes the event data to Kinesis Data Firehose.
  3. Kinesis Data Firehose batches data before sending to S3. An AWS Lambda function transforms the data by adding a new line to each record before sending.
  4. Kinesis Data Firehose delivers the batch of records to S3.
  5. Athena is used to query relevant data from S3 using standard SQL.

The walkthrough shows you how to:

  1. Add the EventBridge app to Freshdesk account.
  2. Configure a Freshworks partner event bus in EventBridge.
  3. Deploy a Kinesis Data Firehose stream, a Lambda function, and an S3 bucket.
  4. Set up a custom rule on the event bus to push data to Kinesis Data Firehose.
  5. Generate sample Freshdesk data to validate the ingestion process.
  6. Set up a table in Athena to query the S3 bucket.
  7. Query and analyze data

Pre-requisites

  • A Freshdesk account (which can be created here).
  • An AWS account.
  • AWS Serverless Application Model (AWS SAM CLI), installed and configured.

Adding the Amazon EventBridge app to a Freshdesk account

  1. Log in to your Freshdesk account and navigate to Admin Helpdesk Productivity Apps. Search for EventBridge:
  2. Choose the Amazon EventBridge icon and choose Install.
  • Enter your AWS account number in the AWS Account ID field.
  • Enter “OnTicketCreate”, “OnTicketUpdate” in the Events field.
  • Enter the AWS Region to send the Freshdesk events in the Region field. This walkthrough uses the us-east-1 Region.

Configuring a Freshworks partner event bus in EventBridge

Once previous step is completed, a partner event source is automatically created in the EventBridge console. Copy the partner event source name to a clipboard.

  1. Clone the GitHub repo and deploy the AWS SAM template:
    git clone https://github.com/aws-samples/amazon-eventbridge-freshdesk-example.git
    cd ./amazon-eventbridge-freshdesk-example
    sam deploy – guided
  2. PartnerEventSource – Enter partner event source name copied from the previous step.
  3. S3BucketName – Enter an S3 bucket name to store Freshdesk ticket event data.

The AWS SAM template creates an association between the partner event source and event bus:

    Type: AWS::Events::EventBus
    Properties:
      EventSourceName: !Ref PartnerEventSource
      Name: !Ref PartnerEventSource

The template creates a Kinesis Data Firehose delivery stream, Lambda function, and S3 bucket to process and store the events from Freshdesk tickets. It also adds a rule to the custom event bus with the Kinesis Data Firehose stream as the target:

  PushToFirehoseRule:
    Type: "AWS::Events::Rule"
    Properties:
      Description: Test Freshdesk Events Rule
      EventBusName: !Ref PartnerEventSource
      EventPattern:
        account: [!Ref AWS::AccountId]
      Name: freshdeskeventrule
      State: ENABLED
      Targets:
        - Arn:
            Fn::GetAtt:
              - "FirehoseDeliveryStream"
              - "Arn"
          Id: "idfreshdeskeventrule"
          RoleArn: !GetAtt EventRuleTargetIamRole.Arn

  EventRuleTargetIamRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Sid: ""
            Effect: "Allow"
            Principal:
              Service:
                - "events.amazonaws.com"
            Action:
              - "sts:AssumeRole"
      Path: "/"
      Policies:
        - PolicyName: Invoke_Firehose
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: "Allow"
                Action:
                  - "firehose:PutRecord"
                  - "firehose:PutRecordBatch"
                Resource:
                  - !GetAtt FirehoseDeliveryStream.Arn

Generating sample Freshdesk data to validate the ingestion process:

To generate sample Freshdesk data, login to the Freshdesk account and browse to the “Tickets” screen as shown:

Follow the steps to simulate two customer service operations:

  1. To create a ticket of type “Refund”. Choose the New button and enter the details:
  2. Update an existing ticket and change the priority to “Urgent”.
  3. Within a few minutes of updating the ticket, the data is pushed via the Freshworks connector to the S3 bucket created using the AWS SAM template. To verify this, browse to the S3 bucket and see that a new object with the ticket data is created:

You can also use the S3 Select option under object actions to view the raw JSON data that is sent from the partner system. You are now ready to analyze the data using Athena.

Setting up a table in Athena to query the S3 bucket

If you are familiar with Apache Hive, you may find creating tables on Athena helpful. You can create tables by writing the DDL statement in the query editor or by using the wizard or JDBC driver. To create a table in Athena:

  1. Copy and paste the following DDL statement in the Athena query editor to create a Freshdesk’s events table. For this example, the table is created in the default database.
  2. Replace S3_Bucket_Name in the following query with the name of the S3 bucket created by deploying the previous AWS SAM template:
CREATE EXTERNAL TABLE ` freshdeskevents`(
  `id` string COMMENT 'from deserializer', 
  `detail-type` string COMMENT 'from deserializer', 
  `source` string COMMENT 'from deserializer', 
  `account` string COMMENT 'from deserializer', 
  `time` string COMMENT 'from deserializer', 
  `region` string COMMENT 'from deserializer', 
  `detail` struct<ticket:struct<subject:string,description:string,is_description_truncated:boolean,description_text:string,is_description_text_truncated:boolean,due_by:string,fr_due_by:string,fr_escalated:boolean,is_escalated:boolean,fwd_emails:array<string>,reply_cc_emails:array<string>,email_config_id:string,id:int,group_id:bigint,product_id:string,company_id:string,requester_id:bigint,responder_id:bigint,status:int,priority:int,type:string,tags:array<string>,spam:boolean,source:int,tweet_id:string,cc_emails:array<string>,to_emails:string,created_at:string,updated_at:string,attachments:array<string>,custom_fields:string,changes:struct<responder_id:array<bigint>,ticket_type:array<string>,status:array<int>,status_details:array<struct<id:int,name:string>>,group_id:array<bigint>>>,requester:struct<id:bigint,name:string,email:string,mobile:string,phone:string,language:string,created_at:string>> COMMENT 'from deserializer')
ROW FORMAT SERDE 
  'org.openx.data.jsonserde.JsonSerDe' 
WITH SERDEPROPERTIES ( 
  'paths'='account,detail,detail-type,id,region,resources,source,time,version') 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION  's3://S3_Bucket_Name/'

The table is created on the data stored in S3 and is ready to be queried. Note that table freshdeskevents points at the bucket s3://S3_Bucket_Name/. As more data is added to the bucket, the table automatically grows, providing a near-real-time data analysis experience.

Querying and analyzing data

You can use the following examples to get started with querying the Athena table.

  1. To get all the events data, run:
SELECT * FROM default.freshdeskevents  limit 10

The preceding output has a detail column containing the details related to the ticket. Tickets can be filtered on nested notations to build more insightful queries. Also, the detail-type column provides classification of tickets as new (onTicketCreate) vs updated (onTicketUpdate).

  1. To show new tickets created today with the type “Refund”:
SELECT detail.ticket.subject,detail.ticket.description_text, detail.ticket.type  FROM default.freshdeskevents
where detail.ticket.type = 'Refund' and "detail-type" = 'onTicketCreate' and date(from_iso8601_timestamp(time)) = date(current_date)
  1. All tickets with an “Urgent” priority but not assigned to an agent:
SELECT "detail-type", detail.ticket.responder_id,detail.ticket.priority, detail.ticket.subject, detail.ticket.type  FROM default.freshdeskevents
where detail.ticket.responder_id is null and detail.ticket.priority = 4

Conclusion

In this blog post, you learn how to configure Freshworks partner event source from the Freshdesk console. Once a partner event source is configured, an AWS SAM template is deployed that creates a custom event bus by attaching the partner event source. A Kinesis Data Firehose, Lambda function, and S3 bucket is used to ingest Freshdesk’s ticket events data for analysis. An EventBridge rule is configured to route the event data to the S3 bucket.

Once event data starts flowing into the S3 bucket, an Amazon Athena table is created to run queries and analyze the ticket events data. Alternative customer service data analysis use cases can be built on the architecture shown in this blog.

To learn more about other partner integrations and the native capabilities of EventBridge, visit the AWS Compute Blog.

Mysterious Macintosh Malware

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/03/mysterious-macintosh-malware.html

This is weird:

Once an hour, infected Macs check a control server to see if there are any new commands the malware should run or binaries to execute. So far, however, researchers have yet to observe delivery of any payload on any of the infected 30,000 machines, leaving the malware’s ultimate goal unknown. The lack of a final payload suggests that the malware may spring into action once an unknown condition is met.

Also curious, the malware comes with a mechanism to completely remove itself, a capability that’s typically reserved for high-stealth operations. So far, though, there are no signs the self-destruct feature has been used, raising the question of why the mechanism exists.

Besides those questions, the malware is notable for a version that runs natively on the M1 chip that Apple introduced in November, making it only the second known piece of macOS malware to do so. The malicious binary is more mysterious still because it uses the macOS Installer JavaScript API to execute commands. That makes it hard to analyze installation package contents or the way that package uses the JavaScript commands.

The malware has been found in 153 countries with detections concentrated in the US, UK, Canada, France, and Germany. Its use of Amazon Web Services and the Akamai content delivery network ensures the command infrastructure works reliably and also makes blocking the servers harder. Researchers from Red Canary, the security firm that discovered the malware, are calling the malware Silver Sparrow.

Feels government-designed, rather than criminal or hacker.

Another article. And the Red Canary analysis.

National Security Risks of Late-Stage Capitalism

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/03/national-security-risks-of-late-stage-capitalism.html

Early in 2020, cyberspace attackers apparently working for the Russian government compromised a piece of widely used network management software made by a company called SolarWinds. The hack gave the attackers access to the computer networks of some 18,000 of SolarWinds’s customers, including US government agencies such as the Homeland Security Department and State Department, American nuclear research labs, government contractors, IT companies and nongovernmental agencies around the world.

It was a huge attack, with major implications for US national security. The Senate Intelligence Committee is scheduled to hold a hearing on the breach on Tuesday. Who is at fault?

The US government deserves considerable blame, of course, for its inadequate cyberdefense. But to see the problem only as a technical shortcoming is to miss the bigger picture. The modern market economy, which aggressively rewards corporations for short-term profits and aggressive cost-cutting, is also part of the problem: Its incentive structure all but ensures that successful tech companies will end up selling insecure products and services.

Like all for-profit corporations, SolarWinds aims to increase shareholder value by minimizing costs and maximizing profit. The company is owned in large part by Silver Lake and Thoma Bravo, private-equity firms known for extreme cost-cutting.

SolarWinds certainly seems to have underspent on security. The company outsourced much of its software engineering to cheaper programmers overseas, even though that typically increases the risk of security vulnerabilities. For a while, in 2019, the update server’s password for SolarWinds’s network management software was reported to be “solarwinds123.” Russian hackers were able to breach SolarWinds’s own email system and lurk there for months. Chinese hackers appear to have exploited a separate vulnerability in the company’s products to break into US government computers. A cybersecurity adviser for the company said that he quit after his recommendations to strengthen security were ignored.

There is no good reason to underspend on security other than to save money — especially when your clients include government agencies around the world and when the technology experts that you pay to advise you are telling you to do more.

As the economics writer Matt Stoller has suggested, cybersecurity is a natural area for a technology company to cut costs because its customers won’t notice unless they are hacked ­– and if they are, they will have already paid for the product. In other words, the risk of a cyberattack can be transferred to the customers. Doesn’t this strategy jeopardize the possibility of long-term, repeat customers? Sure, there’s a danger there –­ but investors are so focused on short-term gains that they’re too often willing to take that risk.

The market loves to reward corporations for risk-taking when those risks are largely borne by other parties, like taxpayers. This is known as “privatizing profits and socializing losses.” Standard examples include companies that are deemed “too big to fail,” which means that society as a whole pays for their bad luck or poor business decisions. When national security is compromised by high-flying technology companies that fob off cybersecurity risks onto their customers, something similar is at work.

Similar misaligned incentives affect your everyday cybersecurity, too. Your smartphone is vulnerable to something called SIM-swap fraud because phone companies want to make it easy for you to frequently get a new phone — and they know that the cost of fraud is largely borne by customers. Data brokers and credit bureaus that collect, use, and sell your personal data don’t spend a lot of money securing it because it’s your problem if someone hacks them and steals it. Social media companies too easily let hate speech and misinformation flourish on their platforms because it’s expensive and complicated to remove it, and they don’t suffer the immediate costs ­– indeed, they tend to profit from user engagement regardless of its nature.

There are two problems to solve. The first is information asymmetry: buyers can’t adequately judge the security of software products or company practices. The second is a perverse incentive structure: the market encourages companies to make decisions in their private interest, even if that imperils the broader interests of society. Together these two problems result in companies that save money by taking on greater risk and then pass off that risk to the rest of us, as individuals and as a nation.

The only way to force companies to provide safety and security features for customers and users is with government intervention. Companies need to pay the true costs of their insecurities, through a combination of laws, regulations, and legal liability. Governments routinely legislate safety — pollution standards, automobile seat belts, lead-free gasoline, food service regulations. We need to do the same with cybersecurity: the federal government should set minimum security standards for software and software development.

In today’s underregulated markets, it’s just too easy for software companies like SolarWinds to save money by skimping on security and to hope for the best. That’s a rational decision in today’s free-market world, and the only way to change that is to change the economic incentives.

This essay previously appeared in the New York Times.

The Problem with Treating Data as a Commodity

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/02/the-problem-with-treating-data-as-a-commodity.html

Excellent Brookings paper: “Why data ownership is the wrong approach to protecting privacy.”

From the introduction:

Treating data like it is property fails to recognize either the value that varieties of personal information serve or the abiding interest that individuals have in their personal information even if they choose to “sell” it. Data is not a commodity. It is information. Any system of information rights­ — whether patents, copyrights, and other intellectual property, or privacy rights — ­presents some tension with strong interest in the free flow of information that is reflected by the First Amendment. Our personal information is in demand precisely because it has value to others and to society across a myriad of uses.

From the conclusion:

Privacy legislation should empower individuals through more layered and meaningful transparency and individual rights to know, correct, and delete personal information in databases held by others. But relying entirely on individual control will not do enough to change a system that is failing individuals, and trying to reinforce control with a property interest is likely to fail society as well. Rather than trying to resolve whether personal information belongs to individuals or to the companies that collect it, a baseline federal privacy law should directly protect the abiding interest that individuals have in that information and also enable the social benefits that flow from sharing information.

On Chinese-Owned Technology Platforms

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/02/on-chinese-owned-technology-platforms.html

I am a co-author on a report published by the Hoover Institution: “Chinese Technology Platforms Operating in the United States.” From a blog post:

The report suggests a comprehensive framework for understanding and assessing the risks posed by Chinese technology platforms in the United States and developing tailored responses. It starts from the common view of the signatories — one reflected in numerous publicly available threat assessments — that China’s power is growing, that a large part of that power is in the digital sphere, and that China can and will wield that power in ways that adversely affect our national security. However, the specific threats and risks posed by different Chinese technologies vary, and effective policies must start with a targeted understanding of the nature of risks and an assessment of the impact US measures will have on national security and competitiveness. The goal of the paper is not to specifically quantify the risk of any particular technology, but rather to analyze the various threats, put them into context, and offer a framework for assessing proposed responses in ways that the signatories hope can aid those doing the risk analysis in individual cases.

Twelve-Year-Old Vulnerability Found in Windows Defender

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/02/twelve-year-old-vulnerability-found-in-windows-defender.html

Researchers found, and Microsoft has patched, a vulnerability in Windows Defender that has been around for twelve years. There is no evidence that anyone has used the vulnerability during that time.

The flaw, discovered by researchers at the security firm SentinelOne, showed up in a driver that Windows Defender — renamed Microsoft Defender last year — uses to delete the invasive files and infrastructure that malware can create. When the driver removes a malicious file, it replaces it with a new, benign one as a sort of placeholder during remediation. But the researchers discovered that the system doesn’t specifically verify that new file. As a result, an attacker could insert strategic system links that direct the driver to overwrite the wrong file or even run malicious code.

It isn’t unusual that vulnerabilities lie around for this long. They can’t be fixed until someone finds them, and people aren’t always looking.

Покана за участие в търг в подкрепа на медийния плурализъм и културното многообразие

Post Syndicated from nellyo original https://nellyo.wordpress.com/2021/02/23/%D0%BF%D0%BE%D0%BA%D0%B0%D0%BD%D0%B0-%D0%B7%D0%B0-%D1%83%D1%87%D0%B0%D1%81%D1%82%D0%B8%D0%B5-%D0%B2-%D1%82%D1%8A%D1%80%D0%B3-%D0%B2-%D0%BF%D0%BE%D0%B4%D0%BA%D1%80%D0%B5%D0%BF%D0%B0-%D0%BD%D0%B0-%D0%BC/

 

Европейската комисия публикува покана за участие в търг със задача да се очертаят съществуващите правила и подходи в подкрепа на медийния плурализъм и културното многообразие, по-специално във връзка с популяризирането на съдържанието от обществен интерес. Проучването ще предостави на Комисията, държавите членки и националните регулаторни органи цялостен анализ, който би могъл да подкрепи общите подходи за популяризиране на съдържанието от общ интерес, както беше обявено през декември в Плана за действие за медиите и аудио-визуалния сектор и Плана за действие за европейската демокрация.

Проучването ще предостави също така подробен преглед на разпределението на приходите от реклама, свързани с медийно съдържание, както онлайн, така и офлайн, между различните участници във веригата за създаване на стойност. То ще предложи възможни методологии за оценка и измерване на медийния плурализъм и ще предложи мерки за преодоляване на недостатъците.

Тази инициатива е част от по-широките усилия за подкрепа на свободата и плурализма на медиите в целия ЕС. Тя се основава на констатациите от Мониторинга на медийния плурализъм (ММП), съфинансиран от ЕС, и на неотдавнашно проучване относно преразгледаната Директива за аудио-визуалните медийни услуги (ДАВМУ), в което се разглеждат по-специално правилата за собствеността върху медиите. Успоредно с това Комисията ще осигури устойчиво финансиране за проекти за медиен плурализъм в рамките на новата програма „Творческа Европа“.

Крайният срок за подаване на оферти е 23 март 2021г.

Повече информация за поканите за представяне на оферти можете да намерите тук.

Списъкът на текущите проекти, финансирани от ЕС в тази област, е достъпен тук.

Dependency Confusion: Another Supply-Chain Vulnerability

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/02/dependency-confusion-another-supply-chain-vulnerability.html

Alex Birsan writes about being able to install malware into proprietary corporate software by naming the code files to be identical to internal corporate code files. From a ZDNet article:

Today, developers at small or large companies use package managers to download and import libraries that are then assembled together using build tools to create a final app.

This app can be offered to the company’s customers or can be used internally at the company as an employee tool.

But some of these apps can also contain proprietary or highly-sensitive code, depending on their nature. For these apps, companies will often use private libraries that they store inside a private (internal) package repository, hosted inside the company’s own network.

When apps are built, the company’s developers will mix these private libraries with public libraries downloaded from public package portals like npm, PyPI, NuGet, or others.

[…]

Researchers showed that if an attacker learns the names of private libraries used inside a company’s app-building process, they could register these names on public package repositories and upload public libraries that contain malicious code.

The “dependency confusion” attack takes place when developers build their apps inside enterprise environments, and their package manager prioritizes the (malicious) library hosted on the public repository instead of the internal library with the same name.

The research team said they put this discovery to the test by searching for situations where big tech firms accidentally leaked the names of various internal libraries and then registered those same libraries on package repositories like npm, RubyGems, and PyPI.

Using this method, researchers said they successfully loaded their (non-malicious) code inside apps used by 35 major tech firms, including the likes of Apple, Microsoft, PayPal, Shopify, Netflix, Yelp, Uber, and others.

Clever attack, and one that has netted him $130K in bug bounties.

More news articles.

GPS Vulnerabilities

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/02/gps-vulnerabilities.html

Really good op-ed in the New York Times about how vulnerable the GPS system is to interference, spoofing, and jamming — and potential alternatives.

The 2018 National Defense Authorization Act included funding for the Departments of Defense, Homeland Security and Transportation to jointly conduct demonstrations of various alternatives to GPS, which were concluded last March. Eleven potential systems were tested, including eLoran, a low-frequency, high-power timing and navigation system transmitted from terrestrial towers at Coast Guard facilities throughout the United States.

“China, Russia, Iran, South Korea and Saudi Arabia all have eLoran systems because they don’t want to be as vulnerable as we are to disruptions of signals from space,” said Dana Goward, the president of the Resilient Navigation and Timing Foundation, a nonprofit that advocates for the implementation of an eLoran backup for GPS.

Also under consideration by federal authorities are timing systems delivered via fiber optic network and satellite systems in a lower orbit than GPS, which therefore have a stronger signal, making them harder to hack. A report on the technologies was submitted to Congress last week.

GPS is a piece of our critical infrastructure that is essential to a lot of the rest of our critical infrastructure. It needs to be more secure.

15 години блог Медийно право

Post Syndicated from nellyo original https://nellyo.wordpress.com/2021/02/20/15-%D0%B3%D0%BE%D0%B4%D0%B8%D0%BD%D0%B8-%D0%B1%D0%BB%D0%BE%D0%B3-%D0%BC%D0%B5%D0%B4%D0%B8%D0%B9%D0%BD%D0%BE-%D0%BF%D1%80%D0%B0%D0%B2%D0%BE/

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Router Security

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/02/router-security.html

This report is six months old, and I don’t know anything about the organization that produced it, but it has some alarming data about router security.

Conclusion: Our analysis showed that Linux is the most used OS running on more than 90% of the devices. However, many routers are powered by very old versions of Linux. Most devices are still powered with a 2.6 Linux kernel, which is no longer maintained for many years. This leads to a high number of critical and high severity CVEs affecting these devices.

Since Linux is the most used OS, exploit mitigation techniques could be enabled very easily. Anyhow, they are used quite rarely by most vendors except the NX feature.

A published private key provides no security at all. Nonetheless, all but one vendor spread several private keys in almost all firmware images.

Mirai used hard-coded login credentials to infect thousands of embedded devices in the last years. However, hard-coded credentials can be found in many of the devices and some of them are well known or at least easy crackable.

However, we can tell for sure that the vendors prioritize security differently. AVM does better job than the other vendors regarding most aspects. ASUS and Netgear do a better job in some aspects than D-Link, Linksys, TP-Link and Zyxel.

Additionally, our evaluation showed that large scale automated security analysis of embedded devices is possible today utilizing just open source software. To sum it up, our analysis shows that there is no router without flaws and there is no vendor who does a perfect job regarding all security aspects. Much more effort is needed to make home routers as secure as current desktop of server systems.

One comment on the report:

One-third ship with Linux kernel version 2.6.36 was released in October 2010. You can walk into a store today and buy a brand new router powered by software that’s almost 10 years out of date! This outdated version of the Linux kernel has 233 known security vulnerabilities registered in the Common Vulnerability and Exposures (CVE) database. The average router contains 26 critically-rated security vulnerabilities, according to the study.

We know the reasons for this. Most routers are designed offshore, by third parties, and then private labeled and sold by the vendors you’ve heard of. Engineering teams come together, design and build the router, and then disperse. There’s often no one around to write patches, and most of the time router firmware isn’t even patchable. The way to update your home router is to throw it away and buy a new one.

And this paper demonstrates that even the new ones aren’t likely to be secure.