Abstract: Every day, hundreds of people fly on airline tickets that have been obtained fraudulently. This crime script analysis provides an overview of the trade in these tickets, drawing on interviews with industry and law enforcement, and an analysis of an online blackmarket. Tickets are purchased by complicit travellers or resellers from the online blackmarket. Victim travellers obtain tickets from fake travel agencies or malicious insiders. Compromised credit cards used to be the main method to purchase tickets illegitimately. However, as fraud detection systems improved, offenders displaced to other methods, including compromised loyalty point accounts, phishing, and compromised business accounts. In addition to complicit and victim travellers, fraudulently obtained tickets are used for transporting mules, and for trafficking and smuggling. This research details current prevention approaches, and identifies additional interventions, aimed at the act, the actor, and the marketplace.
Last month, Wired published a long article about Ray Ozzie and his supposed new scheme for adding a backdoor in encrypted devices. It’s a weird article. It paints Ozzie’s proposal as something that “attains the impossible” and “satisfies both law enforcement and privacy purists,” when (1) it’s barely a proposal, and (2) it’s essentially the same key escrow scheme we’ve been hearing about for decades.
Basically, each device has a unique public/private key pair and a secure processor. The public key goes into the processor and the device, and is used to encrypt whatever user key encrypts the data. The private key is stored in a secure database, available to law enforcement on demand. The only other trick is that for law enforcement to use that key, they have to put the device in some sort of irreversible recovery mode, which means it can never be used again. That’s basically it.
I have no idea why anyone is talking as if this were anything new. Severalcryptographershavealreadyexplained why this key escrow scheme is no better than any other key escrow scheme. The short answer is (1) we won’t be able to secure that database of backdoor keys, (2) we don’t know how to build the secure coprocessor the scheme requires, and (3) it solves none of the policy problems around the whole system. This is the typical mistake non-cryptographers make when they approach this problem: they think that the hard part is the cryptography to create the backdoor. That’s actually the easy part. The hard part is ensuring that it’s only used by the good guys, and there’s nothing in Ozzie’s proposal that addresses any of that.
I worry that this kind of thing is damaging in the long run. There should be some rule that any backdoor or key escrow proposal be a fully specified proposal, not just some cryptography and hand-waving notions about how it will be used in practice. And before it is analyzed and debated, it should have to satisfy some sort of basic security analysis. Otherwise, we’ll be swatting pseudo-proposals like this one, while those on the other side of this debate become increasingly convinced that it’s possible to design one of these things securely.
Already people are using the National Academies report on backdoors for law enforcement as evidence that engineers are developing workable and secure backdoors. Writing in Lawfare, Alan Z. Rozenshtein claims that the report — and a related New York Timesstory — “undermine the argument that secure third-party access systems are so implausible that it’s not even worth trying to develop them.” Susan Landau effectively corrects this misconception, but the damage is done.
Here’s the thing: it’s not hard to design and build a backdoor. What’s hard is building the systems — both technical and procedural — around them. Here’s Rob Graham:
He’s only solving the part we already know how to solve. He’s deliberately ignoring the stuff we don’t know how to solve. We know how to make backdoors, we just don’t know how to secure them.
A bunch of us cryptographers have already explained why we don’t think this sort of thing will work in the foreseeable future. We write:
Exceptional access would force Internet system developers to reverse “forward secrecy” design practices that seek to minimize the impact on user privacy when systems are breached. The complexity of today’s Internet environment, with millions of apps and globally connected services, means that new law enforcement requirements are likely to introduce unanticipated, hard to detect security flaws. Beyond these and other technical vulnerabilities, the prospect of globally deployed exceptional access systems raises difficult problems about how such an environment would be governed and how to ensure that such systems would respect human rights and the rule of law.
The reason so few of us are willing to bet on massive-scale key escrow systems is that we’ve thought about it and we don’t think it will work. We’ve looked at the threat model, the usage model, and the quality of hardware and software that exists today. Our informed opinion is that there’s no detection system for key theft, there’s no renewability system, HSMs are terrifically vulnerable (and the companies largely staffed with ex-intelligence employees), and insiders can be suborned. We’re not going to put the data of a few billion people on the line an environment where we believe with high probability that the system will fail.
In the news, Boeing (an aircraft maker) has been “targeted by a WannaCry virus attack”. Phrased this way, it’s implausible. There are no new attacks targeting people with WannaCry. There is either no WannaCry, or it’s simply a continuation of the attack from a year ago.
It’s possible what happened is that an anti-virus product called a new virus “WannaCry”. Virus families are often related, and sometimes a distant relative gets called the same thing. I know this watching the way various anti-virus products label my own software, which isn’t a virus, but which virus writers often include with their own stuff. The Lazarus group, which is believed to be responsible for WannaCry, have whole virus families like this. Thus, just because an AV product claims you are infected with WannaCry doesn’t mean it’s the same thing that everyone else is calling WannaCry.
Famously, WannaCry was the first virus/ransomware/worm that used the NSA ETERNALBLUE exploit. Other viruses have since added the exploit, and of course, hackers use it when attacking systems. It may be that a network intrusion detection system detected ETERNALBLUE, which people then assumed was due to WannaCry. It may actually have been an nPetya infection instead (nPetya was the second major virus/worm/ransomware to use the exploit).
Or it could be the real WannaCry, but it’s probably not a new “attack” that “targets” Boeing. Instead, it’s likely a continuation from WannaCry’s first appearance. WannaCry is a worm, which means it spreads automatically after it was launched, for years, without anybody in control. Infected machines still exist, unnoticed by their owners, attacking random machines on the Internet. If you plug in an unpatched computer onto the raw Internet, without the benefit of a firewall, it’ll get infected within an hour.
However, the Boeing manufacturing systems that were infected were not on the Internet, so what happened? The narrative from the news stories imply some nefarious hacker activity that “targeted” Boeing, but that’s unlikely.
We have now have over 15 years of experience with network worms getting into strange places disconnected and even “air gapped” from the Internet. The most common reason is laptops. Somebody takes their laptop to some place like an airport WiFi network, and gets infected. They put their laptop to sleep, then wake it again when they reach their destination, and plug it into the manufacturing network. At this point, the virus spreads and infects everything. This is especially the case with maintenance/support engineers, who often have specialized software they use to control manufacturing machines, for which they have a reason to connect to the local network even if it doesn’t have useful access to the Internet. A single engineer may act as a sort of Typhoid Mary, going from customer to customer, infecting each in turn whenever they open their laptop.
Another cause for infection is virtual machines. A common practice is to take “snapshots” of live machines and save them to backups. Should the virtual machine crash, instead of rebooting it, it’s simply restored from the backed up running image. If that backup image is infected, then bringing it out of sleep will allow the worm to start spreading.
Jake Williams claims he’s seen three other manufacturing networks infected with WannaCry. Why does manufacturing seem more susceptible? The reason appears to be the “killswitch” that stops WannaCry from running elsewhere. The killswitch uses a DNS lookup, stopping itself if it can resolve a certain domain. Manufacturing networks are largely disconnected from the Internet enough that such DNS lookups don’t work, so the domain can’t be found, so the killswitch doesn’t work. Thus, manufacturing systems are no more likely to get infected, but the lack of killswitch means the virus will continue to run, attacking more systems instead of immediately killing itself.
One solution to this would be to setup sinkhole DNS servers on the network that resolve all unknown DNS queries to a single server that logs all requests. This is trivially setup with most DNS servers. The logs will quickly identify problems on the network, as well as any hacker or virus activity. The side effect is that it would make this killswitch kill WannaCry. WannaCry isn’t sufficient reason to setup sinkhole servers, of course, but it’s something I’ve found generally useful in the past.
Conclusion Something obviously happened to the Boeing plant, but the narrative is all wrong. Words like “targeted attack” imply things that likely didn’t happen. Facts are so loose in cybersecurity that it may not have even been WannaCry.
The real story is that the original WannaCry is still out there, still trying to spread. Simply put a computer on the raw Internet (without a firewall) and you’ll get attacked. That, somehow, isn’t news. Instead, what’s news is whenever that continued infection hits somewhere famous, like Boeing, even though (as Boeing claims) it had no important effect.
We’re relentlessly innovating on your behalf at AWS, especially when it comes to security. Last November, we launched Amazon GuardDuty, a continuous security monitoring and threat detection service that incorporates threat intelligence, anomaly detection, and machine learning to help protect your AWS resources, including your AWS accounts. Many large customers, including General Electric, Autodesk, and MapBox, discovered these benefits and have quickly adopted the service for its ease of use and improved threat detection. In this post, I want to show you how easy it is for everyone to get started—large and small—and discuss our rapid iteration on the service.
After more than seven years at AWS, I still find myself staying up at night obsessing about unnecessary complexity. Sounds fun, right? Well, I don’t have to tell you that there’s a lot of unnecessary complexity and undifferentiated heavy lifting in security. Most security tooling requires significant care and feeding by humans. It’s often difficult to configure and manage, it’s hard to know if it’s working properly, and it’s costly to procure and run. As a result, it’s not accessible to all customers, and for those that do get their hands on it, they spend a lot of highly-skilled resources trying to keep it operating at its potential.
Even for the most skilled security teams, it can be a struggle to ensure that all resources are covered, especially in the age of virtualization, where new accounts, new resources, and new users can come and go across your organization at a rapid pace. Furthermore, attackers have come up with ingenious ways of giving you the impression your security solution is working when, in fact, it has been completely disabled.
I’ve spent a lot of time obsessing about these problems. How can we use the Cloud to not just innovate in security, but also make it easier, more affordable, and more accessible to all? Our ultimate goal is to help you better protect your AWS resources, while also freeing you up to focus on the next big project.
With GuardDuty, we really turned the screws on unnecessary complexity, distilling continuous security monitoring and threat detection down to a binary decision—it’s either on or off. That’s it. There’s no software, virtual appliances, or agents to deploy, no data sources to enable, and no complex permissions to create. You don’t have to write custom rules or become an expert at machine learning. All we ask of you is to simply turn the service on with a single-click or API call.
GuardDuty operates completely on our infrastructure, so there’s no risk of disrupting your workloads. By providing a hard hypervisor boundary between the code running in your AWS accounts and the code running in GuardDuty, we can help ensure full coverage while making it harder for a misconfiguration or an ingenious attacker to change that. When we detect something interesting, we generate a security finding and deliver it to you through the GuardDuty console and AWS CloudWatch Events. This makes it possible to simply view findings in GuardDuty or push them to an existing SIEM or workflow system. We’ve already seen customers take it a step further using AWS Lambda to automate actions such as changing security groups, isolating instances, or rotating credentials.
Now… are you ready to get started? It’s this simple:
So, you’ve got it enabled, now what can GuardDuty detect?
As soon as you enable the service, it immediately starts consuming multiple metadata streams at scale, including AWS CloudTrail, VPC Flow Logs, and DNS logs. It compares what it finds to fully managed threat intelligence feeds containing the latest malicious IPs and domains. In parallel, GuardDuty profiles all activity in your account, which allows it to learn the behavior of your resources so it can identify highly suspicious activity that suggests a threat.
The threat-intelligence-based detections can identify activity such as an EC2 instance being probed or brute-forced by an attacker. If an instance is compromised, it can detect attempts at lateral movement, communication with a known malware or command-and-control server, crypto-currency mining, or an attempt to exfiltrate data through DNS.
Where it gets more interesting is the ability to detect AWS account-focused threats. For example, if an attacker gets a hold of your AWS account credentials—say, one of your developers exposes credentials on GitHub—GuardDuty will identify unusual account behavior. For example, an unusual instance type being deployed in a region that has never been used, suspicious attempts to inventory your resources by calling unusual patterns of list APIs or describe APIs, or an effort to obscure user activity by disabling CloudTrail logging.
Our obsession with removing complexity meant making these detections fully-managed. We take on all the heavy lifting of building, maintaining, measuring, and improving the detections so that you can focus on what to do when an event does occur.
When we launched at the end of November, we had thirty-four distinct detections in GuardDuty, but we weren’t stopping there. Many of these detections are already on their second or third continuous improvement iteration. In less than three months, we’ve also added twelve more, including nine CloudTrail-based anomaly detections that identify highly suspicious activity in your accounts. These new detections intelligently catch changes to, or reconnaissance of, network, resource, user permissions, and anomalous activity in EC2, CloudTrail, and AWS console log-ins. These are detections we’ve built based on what we’ve learned from observed attack patterns across the scale of AWS.
The intelligence in these detections is built around the identification of highly sensitive AWS API calls that are invoked under one or more highly suspicious circumstances. The combination of “highly sensitive” and “highly suspicious” is important. Highly sensitive APIs are those that either change the security posture of an account by adding or elevating users, user policies, roles, or account-key IDs (AKIDs). Highly suspicious circumstances are determined from underlying models profiled at the API level by GuardDuty. The result is the ability to catch real threats, while decreasing false positives, limiting false negatives, and reducing alert-noise.
It’s still day one
As we like to say in Amazon, it’s still day one. I’m excited about what we’ve built with GuardDuty, but we’re not going to stop improving, even if you’re already happy with what we’ve built. Check out the list of new detections below and all of the GuardDuty detections in our online documentation. Keep the feedback coming as it’s what powers us at AWS.
Now, I have to stop writing because my wife tells me I have some unnecessary complexity to remove from our closet.
New GuardDuty CloudTrail-based anomaly detections
Recon:IAMUser/NetworkPermissions Situation: An IAM user invoked an API commonly used to discover the network access permissions of existing security groups, ACLs, and routes in your AWS account. Description: This finding is triggered when network configuration settings in your AWS environment are probed under suspicious circumstances. For example, if an IAM user in your AWS environment invoked the DescribeSecurityGroups API with no prior history of doing so. An attacker might use stolen credentials to perform this reconnaissance of network configuration settings before executing the next stage of their attack, which might include changing network permissions or making use of existing openings in the network configuration.
Recon:IAMUser/ResourcePermissions Situation: An IAM user invoked an API commonly used to discover the permissions associated with various resources in your AWS account. Description: This finding is triggered when resource access permissions in your AWS account are probed under suspicious circumstances. For example, if an IAM user with no prior history of doing so, invoked the DescribeInstances API. An attacker might use stolen credentials to perform this reconnaissance of your AWS resources in order to find valuable information or determine the capabilities of the credentials they already have.
Recon:IAMUser/UserPermissions Situation: An IAM user invoked an API commonly used to discover the users, groups, policies, and permissions in your AWS account. Description: This finding is triggered when user permissions in your AWS environment are probed under suspicious circumstances. For example, if an IAM user invoked the ListInstanceProfilesForRole API with no prior history of doing so. An attacker might use stolen credentials to perform this reconnaissance of your IAM users and roles to determine the capabilities of the credentials they already have or to find more permissive credentials that are vulnerable to lateral movement.
Persistence:IAMUser/NetworkPermissions Situation: An IAM user invoked an API commonly used to change the network access permissions for security groups, routes, and ACLs in your AWS account. Description: This finding is triggered when network configuration settings are changed under suspicious circumstances. For example, if an IAM user in your AWS environment invoked the CreateSecurityGroup API with no prior history of doing so. Attackers often attempt to change security groups, allowing certain inbound traffic on various ports to improve their ability to access the bot they might have planted on your EC2 instance.
Persistence:IAMUser/ResourcePermissions Situation: An IAM user invoked an API commonly used to change the security access policies of various resources in your AWS account. Description: This finding is triggered when a change is detected to policies or permissions attached to AWS resources. For example, if an IAM user in your AWS environment invoked the PutBucketPolicy API with no prior history of doing so. Some services, such as Amazon S3, support resource-attached permissions that grant one or more IAM principals access to the resource. With stolen credentials, attackers can change the policies attached to a resource, granting themselves future access to that resource.
Persistence:IAMUser/UserPermissions Situation: An IAM user invoked an API commonly used to add, modify, or delete IAM users, groups, or policies in your AWS account. Description: This finding is triggered by suspicious changes to the user-related permissions in your AWS environment. For example, if an IAM user in your AWS environment invoked the AttachUserPolicy API with no prior history of doing so. In an effort to maximize their ability to access the account even after they’ve been discovered, attackers can use stolen credentials to create new users, add access policies to existing users, create access keys, and so on. The owner of the account might notice that a particular IAM user or password was stolen and delete it from the account, but might not delete other users that were created by the fraudulently created admin IAM user, leaving their AWS account still accessible to the attacker.
ResourceConsumption:IAMUser/ComputeResources Situation: An IAM user invoked an API commonly used to launch compute resources like EC2 Instances. Description: This finding is triggered when EC2 instances in your AWS environment are launched under suspicious circumstances. For example, if an IAM user invoked the RunInstances API with no prior history of doing so. This might be an indication of an attacker using stolen credentials to access compute time (possibly for cryptocurrency mining or password cracking). It can also be an indication of an attacker using an EC2 instance in your AWS environment and its credentials to maintain access to your account.
Stealth:IAMUser/LoggingConfigurationModified Situation: An IAM user invoked an API commonly used to stop CloudTrail logging, delete existing logs, and otherwise eliminate traces of activity in your AWS account. Description: This finding is triggered when the logging configuration in your AWS account is modified under suspicious circumstances. For example, if an IAM user invoked the StopLogging API with no prior history of doing so. This can be an indication of an attacker trying to cover their tracks by eliminating any trace of their activity.
UnauthorizedAccess:IAMUser/ConsoleLogin Situation: An unusual console login by an IAM user in your AWS account was observed. Description: This finding is triggered when a console login is detected under suspicious circumstances. For example, if an IAM user invoked the ConsoleLogin API from a never-before- used client or an unusual location. This could be an indication of stolen credentials being used to gain access to your AWS account, or a valid user accessing the account in an invalid or less secure manner (for example, not over an approved VPN).
New GuardDuty threat intelligence based detections
Trojan:EC2/PhishingDomainRequest!DNS This detection occurs when an EC2 instance queries domains involved in phishing attacks.
Trojan:EC2/BlackholeTraffic!DNS This detection occurs when an EC2 instance connects to a black hole domain. Black holes refer to places in the network where incoming or outgoing traffic is silently discarded without informing the source that the data didn’t reach its intended recipient.
Trojan:EC2/DGADomainRequest.C!DNS This detection occurs when an EC2 instance queries algorithmically generated domains. Such domains are commonly used by malware and could be an indication of a compromised EC2 instance.
If you have feedback about this blog post, submit comments in the “Comments” section below. If you have questions about this blog post, start a new thread on the Amazon GuardDuty forum or contact AWS Support.
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