Curriculum
Computer Science and Innovation for Societal Challenges, XXXV series
Grant sponsor
UNIPD
Supervisor

Mauro Conti
Co-supervisor

Luciano Gamberini

Project: Data-driven cybersecurity
Full text of the dissertation book can be downloaded from: https://www.research.unipd.it/handle/11577/3473496

Abstract: Due to the continuous growth in Internet data, cybersecurity practitioners have developed new defenses based on Machine Learning (ML). ML-based solutions offer numerous benefits, from learning patterns among large amounts of data to generalizing to unknown data. This dissertation covers three significant aspects derived from the interaction between machine learning and cybersecurity: (i) definition of novel Network Intrusion Detection Systems (NIDS), (ii) cybersecurity for web content monitoring, and (iii) Adversarial Machine Learning (AML). The first part of the dissertation presents two NIDS themes: XeNIDS, aiming to study and design cross-networking NIDS, and DETONAR, a NIDS for low-powered IoT networks. The second part covers cybersecurity for web content monitoring. In particular, as users interact in forums and Online Social Networks (OSN), their activity might threaten others (e.g., hate speech). The dissertation covers two themes: helpful review prediction, aiming to forecast whether a review from forums (e.g., Amazon, Yelp) will be considered helpful, and PRaNA, a heuristic that leverages videos’ Photo Response Non-Uniformity (PRNU) to spot real videos from their deepfake versions. The third - and last - part of the dissertation presents two evasion attacks: ZeW, an evasion attack on Natural Language Processing applications that leverages invisible UNICODE characters, and CAPA, which discusses real examples of threats created by OSN’s users that undermined Automatic Content Moderators.