Seminar Details
2025-10-16 (13:00) : Malware Detection with Machine Learning: Challenges and Perspectives
At Shannon room, Maxwell building
Organized by Mathematical Engineering
Speakers :
Serena Lucca (ICTEAM)
,
and
Samy Bettaied (ICTEAM)
Abstract :
This presentation explores some challenges and perspectives of applying machine learning to malware detection, drawing insights from two complementary studies. The first investigates the surprising effectiveness of simple models—such as One-Rule and AdaBoost—on state-of-the-art malware detection datasets, revealing that a small subset of dominant features often drives classification performance, leading to minimal differences between simple and deep learning approaches. The second study provides a systematic comparison of tabular and graph-based feature representations under unified conditions, evaluating their trade-offs in computational cost, detection accuracy, and robustness to adversarial attacks. Together, these works question the common assumption that complex models or sophisticated feature types always yield superior results, and instead highlight the importance of understanding feature dominance, dataset biases, and practical constraints in real-world malware detection.