Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness
<|reference_start|>Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness: 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.<|reference_end|>
@article{elshehaby2024introducing,
    title={Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance
  ML Robustness},
    author={Mohamed elShehaby, Aditya Kotha, Ashraf Matrawy},
    journal={arXiv preprint arXiv:2403.10461},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10461},
    primaryClass={cs.LG cs.CR cs.NI}
}