Adversarial Machine Learning in Industrial IoT: A Systematic Review of Attack Realism, Defense Trade-Offs, and Deployment Gaps. [PDF]
Alsaidlani A, Rashid M, Aljabri M.
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AgroPatch AutoStrike for adversarial patch attacks on plant disease detection models. [PDF]
Wu Y +9 more
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A Hybrid Deep Autoencoders and Random Forest Framework for False Data Injection Attack Detection in Industrial Internet of Things Networks. [PDF]
Albarrak AM +3 more
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Hybrid VAE-Transformer-based multi-task learning for cyberattack detection and fault classification in electric vehicle charging stations. [PDF]
Vinitha R, Senthil Kumar R.
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ADS Guard: A Generalizable Defense Framework for Adversarially Robust Occupancy Detection in Smart Buildings. [PDF]
Chaudhari P, Xiao Y, Sun W.
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A Review of Adversarial Example Detection in IoT Sensor Networks: Methods, Evaluation, and Edge Deployment Constraints. [PDF]
Xu W, Li J.
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Going deeper with morphologically detailed neural networks by simulation-based gradient propagation. [PDF]
He G, Du K, Huang T.
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The Evolving Security of the Internet of Vehicles: A Survey from Classical Machine Learning to Large Language Models, Adversarial Robustness, and Explainable AI. [PDF]
Sani MS, Iranmanesh S, Raad R.
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