Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
Luca Demetrio, Battista Biggio, Fabio Roli
Abstract
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different contexts. We discuss how to develop automated and scalable security evaluations of machine learning using practical attacks, reporting a use case on Windows malware detection.
Topics & Concepts
MalwareAdversarial systemComputer scienceAdversarial machine learningComputer securityScalabilityMachine learningMalware analysisArtificial intelligenceOperating systemAdvanced Malware Detection TechniquesAdversarial Robustness in Machine LearningNetwork Security and Intrusion Detection