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

Machine Learning (ML) is susceptible to adversarial attacks that aim to trick ML models, making them produce faulty predictions. Adversarial training was found to increase the robustness of ML models against these attacks. However, in network and cybersecurity, obtaining labeled training and adversarial training data is challenging and costly. Furthermore, concept drift deepens the challenge, particularly in dynamic domains like network and cybersecurity, and requires various models to conduct periodic retraining. This letter introduces Adaptive Continuous Adversarial Training (ACAT) to continuously integrate adversarial training samples into the model during ongoing learning sessions, using real-world detected adversarial data, to enhance model resilience against evolving adversarial threats. ACAT is an adaptive defense mechanism that utilizes periodic retraining to effectively counter adversarial attacks while mitigating catastrophic forgetting. Our approach also reduces the total time required for adversarial sample detection, especially in environments such as network security where the rate of attacks could be very high. Traditional detection processes that involve two stages may result in lengthy procedures. Experimental results using a SPAM detection dataset demonstrate that with ACAT, the accuracy of the SPAM filter increased from 69% to over 88% after just three retraining sessions. Furthermore, ACAT outperforms conventional adversarial sample detectors, providing faster decision times, up to four times faster in some cases.