Research paper shows it may be possible to distinguish malware traffic using TLS

Posted by   Martijn Grooten on   Jun 22, 2017

Researchers at Cisco have published a paper (PDF) describing how it may be possible to use machine learning to distinguish malware command-and-control (C&C) traffic using TLS from regular enterprise traffic, and to classify malware families based on their encrypted C&C traffic.

The need for malware to communicate with its operators, so that it can receive instructions and exfiltrate information from infected systems, is a weak point – it can't easily hide its activity from security products scanning network traffic. For this reason, the trend among malware of using SSL/TLS – the protocol over which a significant portion of today's web and email traffic is sent – is an understandable one.

A good encryption protocol makes encrypted content indistinguishable from random noise, but while TLS uses top-class encryption standards, it cannot avoid the use of metadata that can give away some essential details of the communication.

Even if one ignores the remote IP address and the domain sent in the certificate, both of which can help detect a known malware family, TLS includes explicit metadata, such as the cipher suites and TLS extensions offered and used, as well as more implicit metadata, such as the length and frequency of the packets and the variation seen in them.

The Cisco researchers trained their machine-learning classifier using a combination of malicious TLS traffic and legitimate enterprise TLS traffic. The classifier was able to identify the TLS traffic of most malware families with high accuracy – even that of families that had not been present in the training set.

tlsmalwareindicators.png

The research is very much a work-in-progress and, as befits a good research paper, its authors openly admit the limitations to their work. For instance, the malware was run in Windows XP-based sandboxes, which could have helped the detection: malware often inherits TLS properties from the operating system in which it runs. At the same time, malware is mostly likely to live on older operating systems, making this set-up not too different from a real-world scenario.

It is also important to note that the classifier was not able to say anything about the content of the traffic; it would thus be useless as part of a data-loss prevention system. TLS, especially its most recent versions, is one of the strongest Internet protocols, and the fact that it properly protects  content is a very good thing, even if it can be frustrating for malware analysis and detection.

At Virus Bulletin, we have repeatedly shown how malicious web traffic can be blocked by security products. Organizations using a web security gateway will have to make a decision as to whether to have it inspect TLS-encrypted web traffic as well. While I think that, in most scenarios, inspecting the traffic is a compromise worth making, this research shows that one may be able to block malware's ability to connect to its owners without being able to decrypt the traffic.

twitter.png
fb.png
linkedin.png
hackernews.png
reddit.png

 

Latest posts:

AfricaHackon 2019: a great event and a reminder that security is global

Last week, VB Editor Martijn Grooten travelled to the Kenyan capital Nairobi to speak at the 6th edition of the AfricaHackon event.

Virus Bulletin researcher discovers new Lord exploit kit

Still in-development kit thus far only targets Flash Player vulnerabilities

VB2019 call for last-minute papers opened

The call for last-minute papers for VB2019 is now open. Submit before 1 September to have your abstract considered for one of the nine slots reserved for 'hot' research.

Nominations opened for sixth Péter Szőr Award

Virus Bulletin is seeking nominations for the sixth annual Péter Szőr Award.

Haroon Meer and Adrian Sanabria to deliver VB2019 closing keynote

New additions to the VB2019 conference programme include a closing keynote address from Thinkst duo Haroon Meer and Adrian Sanabria and a talk on attacks against payment systems.

We have placed cookies on your device in order to improve the functionality of this site, as outlined in our cookies policy. However, you may delete and block all cookies from this site and your use of the site will be unaffected. By continuing to browse this site, you are agreeing to Virus Bulletin's use of data as outlined in our privacy policy.