Author: Ruud van Luijk

Attacks need to have a form of communication with their victim machines, also known as Command and Control (C2) [1]. This can be in the form of a continuous connection or connect the victim machine directly. However, it’s convenient to have the victim machine connect to you. In other words: It has to communicate back. This blog describes a method to detect one technique utilized by many popular attack frameworks based solely on connection metadata and statistics, in turn enabling this technique to be used on multiple log sources.

Many attack frameworks use beaconing

Frameworks like Cobalt Strike, PoshC2, and Empire, but also some run-in-the-mill malware, frequently check-in at the C2 server to retrieve commands or to communicate results back. In Cobalt Strike this is called a beacon, but concept is similar for many contemporary frameworks. In this blog the term ‘beaconing’ is used as a general term for the call-backs of malware. Previous fingerprinting techniques shows that there are more than a thousand Cobalt Strike servers online in a month that are actively used by several threat actors, making this an important point to focus on.

While the underlying code differs slightly from tool to tool, they often exist of two components to set up a pattern for a connection: a sleep and a jitter. The sleep component indicates how long the beacon has to sleep before checking in again, and the jitter modifies the sleep time so that a random pattern emerges. For example: 60 seconds of sleep with 10% jitter results in a uniformly random sleep between 54 and 66 seconds (PoshC2 [3], Empire [4]) or a uniformly random sleep between 54 and 60 seconds (Cobalt Strike [5]). Note the slight difference in calculation.

This jitter weakens the pattern but will not dissolve the pattern entirely. Moreover, due to the uniform distribution used for the sleep function the jitter is symmetrical. This is in our advantage while detecting this behaviour!

Detecting the beacon

While static signatures are often sufficient in detecting attacks, this is not the case for beaconing. Most frameworks are very customizable to your needs and preferences. This makes it hard to write correct and reliable signatures. Yet, the pattern does not change that much. Therefore, our objective is to find a beaconing pattern in seemingly pattern less connections in real-time using a more anomaly-based method. We encourage other blue teams/defenders to do the same.

Since the average and median of the time between the connections is more or less constant, we can look for connections where the times between consecutive connections constantly stay within a certain range. Regular traffic should not follow such pattern. For example, it makes a few fast-consecutive connections, then a longer time pause, and then again, some interaction. Using a wider range will detect the beacons with a lot of jitter, but more legitimate traffic will also fall in the wider range. There is a clear trade-off between false positives and accounting for more jitter.

In order to track the pattern of connections, we create connection pairs. For example, an IP that connects to a certain host, can be expressed as ’10.0.0.1 -> somerandomhost.com”. This is done for all connection pairs in the network. We will deep dive into one connection pair.

The image above illustrates a beacon is simulated for the pair ’10.0.0.1 -> somerandomhost.com” with a sleep of 1 second and a jitter of 20%, i.e. having a range between 0.8 and 1.2 seconds and the model is set to detect a maximum of 25% jitter. Our model follows the expected timing of the beacon as all connections remain within the lower and upper bound. In general, the more a connection reside within this bandwidth, the more likely it is that there is some sort of beaconing. When a beacon has a jitter of 50% our model has a bandwidth of 25%, it is still expected that half of the beacons will fall within the specified bandwidth.

Even when the configuration of the beacon changes, this method will catch up. The figure above illustrates a change from one to two seconds of sleep whilst maintaining a 10% beaconing. There is a small period after the change where the connections break through the bandwidth, but after several connections the model catches up.

This method can work with any connection pair you want to track. Possibilities include IPs, HTTP(s) hosts, DNS requests, etc. Since it works on only the metadata, this will also help you to hunt for domain fronted beacons (keeping in mind your baseline).

Keep in mind the false positives

Although most regular traffic will not follow a constant pattern, this method will most likely result in several false positives. Every connection that runs on a timer will result in the exact same pattern as beaconing. Example of such connections are windows telemetry, software updates, and custom update scripts. Therefore, some baselining is necessary before using this method for alerting. Still, hunting will always be possible without baselining!

Conclusion

Hunting for C2 beacons proves to be a worthwhile exercise. Real world scenarios confirm the effectiveness of this approach. Depending on the size of the network logs, this method can plow through a month of logs within an hour due to the simplicity of the method. Even when the hunting exercise did not yield malicious results, there are often other applications that act on specific time intervals and are also worth investigating, removing, or altering. While this method will not work when an adversary uses a 100% jitter. Keep in mind that this will probably annoy your adversary, so it’s still a win!

References:

[1]. https://attack.mitre.org/tactics/TA0011/

[2]. https://blog.fox-it.com/2019/02/26/identifying-cobalt-strike-team-servers-in-the-wild/

[3]. https://github.com/nettitude/PoshC2/blob/master/C2-Server.ps1

https://github.com/nettitude/PoshC2_Python/blob/4aea6f957f4aec00ba1f766b5ecc6f3d015da506/Files/Implant-Core.ps1

[4]. https://github.com/EmpireProject/Empire/blob/master/data/agent/agent.ps1

[5]. https://www.cobaltstrike.com/help-beacon