This is an overview of a series of 6 blog posts we dedicated to the analysis and decryption of Cobalt Strike traffic. We include videos for different analysis methods.
In part 1, we explain that Cobalt Strike traffic is encrypted using RSA and AES cryptography, and that we found private RSA keys that can help with decryption of Cobalt Strike traffic
In part 2, we actually decrypt traffic using private keys. Notice that one of the free, open source tools that we created to decrypt Cobalt Strike traffic, cs-parse-http-traffic.py, was a beta release. It has now been replaced by tool cs-parse-traffic.py. This tool is capable to decrypt HTTP(S) and DNS traffic. For HTTP(S), it’s a drop-in replacement for cs-parse-http-traffic.py.
In part 3, we use process memory dumps to extract the decryption keys. This is for use cases where we don’t have the private keys.
In part 4, we deal with some specific obfuscation: data transforms of encrypted traffic, and sleep mode in beacons’ process memory.
In part 5, we handle Cobalt Strike DNS traffic.
And finally, in part 6, we provide some tips to make memory dumps of Cobalt Strike beacons.
The tools used in these blog post are free and open source, and can be found here.
Here are a couple of videos that illustrate the methods discussed in this series:
- Using Known Private Keys To Decrypt Traffic
- Using Process Memory To Decrypt Traffic
- Dealing With Obfuscated Traffic And Process Memory
- Decrypting DNS Traffic
YouTube playlist “Cobalt Strike: Decrypting Traffic“
Blog posts in this series:
- Cobalt Strike: Using Known Private Keys To Decrypt Traffic – Part 1
- Cobalt Strike: Using Known Private Keys To Decrypt Traffic – Part 2
- Cobalt Strike: Using Process Memory To Decrypt Traffic – Part 3
- Cobalt Strike: Decrypting Obfuscated Traffic – Part 4
- Cobalt Strike: Decrypting DNS Traffic – Part 5
- Cobalt Strike: Memory Dumps – Part 6
About the authors
Didier Stevens is a malware expert working for NVISO. Didier is a SANS Internet Storm Center senior handler and Microsoft MVP, and has developed numerous popular tools to assist with malware analysis. You can find Didier on Twitter and LinkedIn.
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