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AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks. (arXiv:2203.09792v1 [cs.LG])
March 21, 2022, 1:20 a.m. | Arman Pashamokhtari, Gustavo Batista, Hassan Habibi Gharakheili
cs.CR updates on arXiv.org arxiv.org
Machine Learning-based techniques have shown success in cyber intelligence.
However, they are increasingly becoming targets of sophisticated data-driven
adversarial attacks resulting in misprediction, eroding their ability to detect
threats on network devices. In this paper, we present AdIoTack, a system that
highlights vulnerabilities of decision trees against adversarial attacks,
helping cybersecurity teams quantify and refine the resilience of their trained
models for monitoring IoT networks. To assess the model for the worst-case
scenario, AdIoTack performs white-box adversarial learning to launch …
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