DNS is a great source of information for security analysts… if you’re not already monitoring DNS activity in your network — you should start asap, for the reasons we’ll explore in this article
DNS is one of the major workhorses that powers the Internet. Everything uses DNS — browsers, apps, updates… and malware. Almost every malware needs to “phone home” to receive instructions, exfiltrate data or otherwise communicate with attackers. Malware authors utilize a variety of DNS tricks to control the malware they spread, such as:
- Domain Generation Algorithms
- Fast Flux Domains
- DNS Covert Channels
- Familiar or misspelt domains
Domain Generation Algorithms
Once malware is installed on a target system, it usually needs to communicate back to its C&C server for instructions. Hardcoding a domain into the malware would make the malware very short lived… malicious activity is banned on many hosting providers who are quick to revoke DNS records involved in such activity. So, authors program malware using Domain Generation Algorithms [DGAs] to generate algorithms on the fly, usually by using a combination of the current date. This increases the chances of malware being able to communicate with a DNS domain that is still active — increasing it’s useful lifespan. Wikipedia has a very interesting and easy to read article on DGA, including examples from CornFlicker, CryptoLocker, and others.
Typically these domains are quite random in nature, for example “intgmxdeadnxuyla” and “axwscwsslmiagfah”. That makes it quite easy for a human to pick out. The downside is that there are way too many DNS records to check manually, and automated malware domain lists don’t usually include DGA generated domains, there are simply too many and they change every day
CyberSift helps out by performing a number of language structure checks on visited domains. In the example below we investigate a DGA generated domain that has quite a high abnormal score of 22.853 (anything above a 10.0 warrants some investigation). As you can see in the highlighted portion of our output, a model named “Score” contributed quite a large number of points (13.515) to the abnormal score. The score model uses statistical analysis to determine how likely a domain has a given structure, and how often it is used. Since DGAs output random domains that dont usually look anything like proper language, they tend to trigger this model
Fast flux domains are a tried and tested malware and phishing technique. In it’s simplest form, malware authors register hundreds (or thousands) of IP address for a given domain. The DNS records are given a short time to live, so that infected victims connect to different IP addresses for any given malware domain, reducing the chances of an IP address being blocked. Sometimes, fast flux domains are used in combination with DGA to further increase the chances of infected machines communicating back to their handlers.
Some advertising networks (usually shady ones…) tend to use fast-flux domains, which return a high number of IP addresses. In our example below we see one such advertising network, returning an abnormally high number of IP addresses, and contributing quite a large chunk to the anomaly score
DNS Covert Channels
DNS covert channels are particularly interesting. We mentioned that practically any program needing to connect to the Internet requires DNS to function properly, so two things are pretty much guaranteed:
- Outbound DNS requests are not blocked
- Since DNS proxies are not as prolific as HTTP/S proxies, DNS traffic is very probably not monitored
This creates a perfect medium for attackers to use… by tunneling data through DNS traffic. As recently as last month (March 2017), security researches have observed malware leveraging this concept to hide their communication traffic in plain sight:
DNSMessenger used TXT records to create a covert channel, that would be difficult to detect. Looking at CyberSift’s output during testing, we see an alert that there was anomalous activity in our environment’s DNS activity:
CyberSift uses intelligent clustering techniques to detect anomalous behavior when it comes to the amount of DNS queries being sent by your environment, to detect issues such as mis-configuration, or — as in the example above — covert malicious activity
This makes threat-hunting quite easy. A sample security threat hunter’s workflow using CyberSift would be something as follows:
Familiar or misspelt domains
You’ve definitely seen this age-old but extremely popular technique from scam and phishing campaigns. Attackers love to use familiar looking domains such as “service-portal-paypal.com” (or “papyal.com”) and similar domains which most analysts wouldn’t give a second glance to, since they’d assume it’s a legitimate service. This has become quite a problem especially with free SSL certificate authorities:
An example of this technique in use in the wild can be seen in the “OilRig” campaign:
From the section “Indicator of Compromise” in the above screenshot, we see a domain which certainly looks familiar: main-google-resolver.com. An analyst is certainly forgiven if they don’t investigate this domain more thoroughly — it does after all look like something Google would really use.
But a closer look with CyberSift reveals a different story:
Again, the “Score” model comes to the rescue here. The model realizes that the word “google” is usually not seen in this context, and adds just enough point to make this domain anomalous (remember anything over 10.0 is considered worth investigating). Over and above that, the site in this case did not resolve, so this caused the DNS anomaly engine to push up the anomaly score even further.