  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.
