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Cerber Ransomware Now Evades Machine Learning
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LucasZa
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LucasZa,
User Rank: Moderator
3/29/2017 | 5:21:50 PM
More FUD from legacy AV vendors about Machine Learning

First, let me be open and transparent by saying I work at Cylance as a pre-sales technical engineer. As a result, I have full access to our product and can test against malware. Here's my analysis and the results based on the samples Trend refers to in their post. The data below is unbiased, raw testing results.

 

f4dbbb2c4d83c2bbdf4faa4cf6b78780b01c2a2c59bc399e5b746567ce6367dd is a self extracting zip archive containing the other three files. Once it extracts, it automatically runs the vbs file it drops, 38oDr5.vbs. Cylance Protect's Script Control feature blocked the vbs file from ever executing. Threat neutralized pre-execution.

 

09ef4c6b8a297bf4cf161d4c12260ca58cc7b05eb4de6e728d55a4acd94606d4 is 38oDr5.vbs. For the sake of following the malware execution to see where else we'll block it, I completely disabled our Script Control feature, then tested again. When the vbs runs, it drops and tries to execute the malware dll, 8iqv.dll using the command:  "rundll32","8ivq.dll arzy949". We block and quarantine the 8iqv.dll malware sample pre-execution thanks to the industries most mature and advanced machine learning math model. Threat neutralized pre-execution.

 

e3e5d9f1bacc4f43af3fab28a905fa4559f98e4dadede376e199360d14b39153 is the 8ivq.dll our machine learning math model blocked pre-execution.

 

a61eb7c8d7a6bc9e3eb2b42e7038a0850c56e68f3fec0378b2738fe3632a7e4c has filename "x" without an extension. It's an obfuscated or crypted file with the malware's configuration parameters as well as a second stage malware component that gets loaded by the dll. It's the configuration file used by the malware. Since the malware was neutralized pre-execution at two different pre-execution points, there's no running dll malware to decrypt and run the content of X. The Cylance protected endpoint remained unscathed.

 

Now here's the kicker. The agent version I'm using in my lab hasn't been updated since November and the math model used to determine good from bad was created last summer. The results I describe were observed offline after reverting to my November snapshot and disabling network connectivity. The math is in the agent and it's future-proof. Now that's some real machine learning for you!



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