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A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection.

, , , , and . FPS, volume 13291 of Lecture Notes in Computer Science, page 191-207. Springer, (2021)

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An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection., , , and . CoRR, (2024)Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review., , , , and . CoRR, (2023)A Low-Cost Multi-Agent System for Physical Security in Smart Buildings., , , , and . CoRR, (2022)Towards adversarial realism and robust learning for IoT intrusion detection and classification., , and . Ann. des Télécommunications, 78 (7-8): 401-412 (August 2023)A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection., , , , and . FPS, volume 13291 of Lecture Notes in Computer Science, page 191-207. Springer, (2021)Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection., , and . CoRR, (2022)A Multi-policy Framework for Deep Learning-based Fake News Detection., , , , and . DCAI (1), volume 583 of Lecture Notes in Networks and Systems, page 121-130. Springer, (2022)Unravelling Network-Based Intrusion Detection: A Neutrosophic Rule Mining and Optimization Framework., , , , , and . ESORICS Workshops (2), volume 14399 of Lecture Notes in Computer Science, page 59-75. Springer, (2023)SoK: Realistic adversarial attacks and defenses for intelligent network intrusion detection., , and . Comput. Secur., (November 2023)From Data to Action: Exploring AI and IoT-Driven Solutions for Smarter Cities., , , , , and . DCAI (1), volume 740 of Lecture Notes in Networks and Systems, page 44-53. Springer, (2023)