LARAR enhances adversarial robustness in network intrusion detection by using layer-wise adaptive regularization and auxiliary classifiers, achieving 95.01% clean accuracy and improved defense against FGSM, PGD, and transfer attacks on UNSW-NB15.
In: 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp
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Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach
LARAR enhances adversarial robustness in network intrusion detection by using layer-wise adaptive regularization and auxiliary classifiers, achieving 95.01% clean accuracy and improved defense against FGSM, PGD, and transfer attacks on UNSW-NB15.