  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.
