期刊
ENTROPY
卷 23, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/e23040395
关键词
coevolution; adversarial machine learning; malware arm race; EEE; VirusTotal; hothouse
资金
- EPSRC [SeMaMatch EP/K032623/1, DAASE EP/J017515/1, LUCID EP/P005659/1, InfoTestSS EP/P005888/1]
- EPSRC [EP/K032623/1] Funding Source: UKRI
Malware detection is an evolving arms race where attackers and defenders constantly seek advantage. White hats must be conservative to avoid false positives, while black hats usually only need to make incremental changes to evade detection. The method presented aims to accelerate the white hat's ability to counter the black hat's moves, forcing black hats to make disruptive moves more often.
Malware detection is in a coevolutionary arms race where the attackers and defenders are constantly seeking advantage. This arms race is asymmetric: detection is harder and more expensive than evasion. White hats must be conservative to avoid false positives when searching for malicious behaviour. We seek to redress this imbalance. Most of the time, black hats need only make incremental changes to evade them. On occasion, white hats make a disruptive move and find a new technique that forces black hats to work harder. Examples include system calls, signatures and machine learning. We present a method, called Hothouse, that combines simulation and search to accelerate the white hat's ability to counter the black hat's incremental moves, thereby forcing black hats to perform disruptive moves more often. To realise Hothouse, we evolve EEE, an entropy-based polymorphic packer for Windows executables. Playing the role of a black hat, EEE uses evolutionary computation to disrupt the creation of malware signatures. We enter EEE into the detection arms race with VirusTotal, the most prominent cloud service for running anti-virus tools on software. During our 6 month study, we continually improved EEE in response to VirusTotal, eventually learning a packer that produces packed malware whose evasiveness goes from an initial 51.8% median to 19.6%. We report both how well VirusTotal learns to detect EEE-packed binaries and how well VirusTotal forgets in order to reduce false positives. VirusTotal's tools learn and forget fast, actually in about 3 days. We also show where VirusTotal focuses its detection efforts, by analysing EEE's variants.
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