2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detection
Sabrine Ennaji, Elhadj Benkhelifa, Nadia Kabachi
Machine learning-based intrusion detection systems (IDS) are critical for securing Industrial Internet of Things (IIoT) environments. Most adversarial research against them perturb…
Behavior-Aware and Generalizable Defense Against Black-Box Adversarial Attacks for ML-Based IDS
Sabrine Ennaji, Elhadj Benkhelifa, Luigi Vincenzo Mancini
Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs using indirect feedback such as…
Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS
Sabrine Ennaji, Elhadj Benkhelifa, Luigi V. Mancini
Adversarial attacks, wherein slight inputs are carefully crafted to mislead intelligent models, have attracted increasing attention. However, a critical gap persists between theore…
Toward Realistic Adversarial Attacks in IDS: A Novel Feasibility Metric for Transferability
Sabrine Ennaji, Elhadj Benkhelifa, Luigi Vincenzo Mancini
Transferability-based adversarial attacks exploit the ability of adversarial examples, crafted to deceive a specific source Intrusion Detection System (IDS) model, to also mislead…
Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects
Sabrine Ennaji, Fabio De Gaspari, Dorjan Hitaj +2
Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed t…