Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance
  ML Robustness
Mohamed elShehaby Aditya Kotha Ashraf Matrawy
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.