20 hours a week
Strong math background, knowledge of Python or similar language, some machine Learning notions might be useful
Machine learning is a popular approach to signature-less malware detection because it can generalize to new (unseen) malware families. Some recent works have proposed the use of AI/ML-powered malware to bypass machine learning anti-malware systems (for instance, adversarial machine learning).
The goal of the project is to model the system of malware vs anti-malware systems as two opponents using various AI/ML strategies to bypass the other side, such as adversarial machine learning. We would like to model this system using a mathematical (probabilistic) model to understand how the real system evolves, e.g. whether it converges to a stable state (and who benefits from this state: attackers or defenders?) or to a never-ending game, or whether the convergence (if any) depends on some initial assumptions.