CARD: Robustness-Preserving Transfer Learning for Network Intrusion Detection via Contrastive Adversarial Representation Distillation

Robust neural networks are essential to build network intrusion detection systems resilient to evasion attacks. Learning such models via adversarial training demands extensive labeled data and high model capacity, making it impractical in evolving, resource-constrained threat environments. Transfer learning (TL) uses pre-trained models to enhance downstream tasks, offering a promising mitigation approach. However, most TL approaches prioritize performance on clean examples without addressing robustness against adversarial examples and random variations. Our empirical study reveals that standard fine-tuning and distillation often yield accurate but not robust models, while the few existing adversarial TL provide limited robustness. In this article, we propose a novel robustness-preserving TL framework, Contrastive Adversarial Representation Distillation (CARD), to generate a robust target model by transferring robustness and performance from a robust source model into the target task. CARD tackles three issues: (i) target domain data scarcity; (ii) differences in data domains and model architectures between target and source tasks; and (iii) target model robustness against static and adaptive evasion attacks, and natural corruptions. Experiments on binary and multiclass detection show that CARD outperforms state-of-the-art methods in various TL tasks across data domains and model architectures when only 5% training data is available, achieving 17.67$\%$ and 8.38$\%$ higher adversarial robust accuracy as well as 9.75$\%$ and 11.42$\%$ higher natural robust accuracy than adversarial fine-tuning and distillation.