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Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification. (arXiv:2301.13122v2 [cs.CR] UPDATED)
cs.CR updates on arXiv.org arxiv.org
The Internet of Things (IoT) faces tremendous security challenges. Machine
learning models can be used to tackle the growing number of cyber-attack
variations targeting IoT systems, but the increasing threat posed by
adversarial attacks restates the need for reliable defense strategies. This
work describes the types of constraints required for a realistic adversarial
cyber-attack example and proposes a methodology for a trustworthy adversarial
robustness analysis with a realistic adversarial evasion attack vector. The
proposed methodology was used to evaluate three …
adversarial adversarial attacks analysis attack attacks challenges classification constraints cyber cyber-attack defense defense strategies detection internet internet of things intrusion intrusion detection iot machine machine learning machine learning models robustness security security challenges systems targeting things threat types work