Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness

Mohamed elShehaby; Aditya Kotha; Ashraf Matrawy

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arXiv · 2403.10461

Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness

Mohamed elShehaby·Aditya Kotha·Ashraf Matrawy

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Abstract

Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in network/cybersecurity domains is challenging and costly. Therefore, this letter introduces Adaptive Continuous Adversarial Training (ACAT), a method that integrates adversarial training samples into the model during continuous learning sessions using real-world detected adversarial data. Experimental results with a SPAM detection dataset demonstrate that ACAT reduces the time required for adversarial sample detection compared to traditional processes. Moreover, the accuracy of the under-attack ML-based SPAM filter increased from 69% to over 88% after just three retraining sessions.

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Mohamed elShehaby, Aditya Kotha, Ashraf Matrawy. 2024-03-15. Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness. https://arxiv.org/abs/2403.10461

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