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- W3216486624 abstract "With the rapid growth of malware attacks, more antivirus developers consider deploying machine learning technologies into their productions. Researchers and developers published various machine learning-based detectors with high precision on malware detection in recent years. Although numerous machine learning-based malware detectors are available, they face various machine learning-targeted attacks, including evasion and adversarial attacks. This project explores how and why adversarial examples evade malware detectors, then proposes a randomised chaining method to defend against adversarial malware statically. This research is crucial for working towards combating the pertinent malware cybercrime." @default.
- W3216486624 created "2021-12-06" @default.
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- W3216486624 date "2022-02-14" @default.
- W3216486624 modified "2023-09-26" @default.
- W3216486624 title "Statically Detecting Adversarial Malware through Randomised Chaining" @default.
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- W3216486624 doi "https://doi.org/10.1145/3511616.3513103" @default.
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