Current students


BERNASCONI de LUCA MARTINOCycle: XXXVI

Section: Computer Science and Engineering
Advisor: TROVO' FRANCESCO
Tutor: GATTI NICOLA

Major Research topic:
Adversarial Machine Learning Techniques for Cybersecurity

Abstract:
Adversarial Machine Learning (AML) is an effective framework to study the robustness of Machine Learning (ML) models in adversarial settings. Indeed, ML techniques may fail in domains such as cybersecurity, where the data is poisoned by an opponent. This project aims to formulate the AML framework using Game Theory to handle the interplay between the learner and the attacker. The effectiveness of the proposed framework will be assessed by designing novel methods for cybersecurity applications.