A new version of the Cerber ransomware family has adopted new techniques to make itself harder to detect by endpoint security software that uses machine learning for detection. It is now using a new loader designed to hollow out a normal process where the code of Cerber is run instead.
Cerber reared its ugly head March last year and has rapidly grabbed marketshare by furiously innovating and using different attack vectors and distributions channels. It spreads mostly through phishing emails, but also uses exploit kits.
In August 2016, Invincea researchers discovered that Cerber was being distributed by Betabot, which was designed as a banking information stealing Trojan but recycled for ransomware. Recently, Cyren researchers reported that Cerber is being dropped by Kovter, a click-fraud Trojan which was dropping Locky several months ago.
Trend Micro reports that the new loader was necessary because machine learning security code detect malicious files based on features instead of signatures. The new packaging and loading mechanism employed by Cerber can cause problems for static machine learning approaches–i.e, methods that analyze a file without any execution or emulation. In other words, the way Cerber is packaged could be said to be designed to evade machine learning file detection. For every new malware detection technique, an equivalent evasion technique is created out of necessity.
The good news, the researchers say, is that this new evasion technique can be defeated by security approaches that employ multiple layers of protection, because the attack has other weaknesses, such as the use of an unpacked .DLL file. Solutions that don’t overly rely on machine learning can still prove effective against this threat.
And of course you want to step all your end-users through new-school security awareness training, so that they spot the phishing emails when they make it through all the filters.
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