Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection
During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems.However, a non-negligible limitation of machine learning methods used to train decision models is that adversarial attacks can easily fool them.Advers