@inproceedings{guerzizDomainAdversarialNeural2026,

title = {Domain Adversarial Neural Networks with Adversarial Robustness Evaluation for Intrusion Detection Systems},

author = {I. Guerziz and T. Falk and L. B. Le and Z. A. E. Houda},

editor = {Adi K. and Nguena Timo O. and Boulahia-Cuppens N. and Espes D. and Stakhanova N. and Omar M.},

url = {https://www.scopus.com/pages/publications/105046102448?origin=resultslist},

doi = {10.1007/978-3-032-20732-6_10},

isbn = {03029743 (ISSN); 978-303220731-9 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {16295 LNCS},

pages = {153–165},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Modern Network Intrusion Detection Systems (NIDS) face the dual challenge of maintaining performance across diverse network environments while resisting adversarial manipulations. This paper investigates the intersection of domain adaptation and adversarial robustness in NIDS, a topic that has not been extensively studied. We implement a Domain-Adversarial Neural Network (DANN) with dynamic gradient reversal to adapt models from NSL-KDD to UNSW-NB15. To evaluate security, we assess the model under Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks. Additionally, we introduce FGSM-based adversarial training to enhance robustness. Our results show that while domain adaptation improves cross-domain detection, it also increases susceptibility to adversarial attacks. Incorporating adversarial training mitigates this vulnerability, improving resilience without compromising performance on clean data. These findings provide key insights for designing adaptive and secure intrusion detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},

note = {Journal Abbreviation: Lect. Notes Comput. Sci.},

keywords = {Adversarial Attacks, Adversarial neural network, Adversarial Neural Networks, Attack Resilience, Attack resiliences, Computer crime, Domain adaptation, Fast gradient sign method, FGSM, Gradient-descent, Intrusion Detection, Intrusion Detection Systems, Network intrusion, Network intrusion detection systems, Network security, Neural networks, Neural-networks, PGD, Projected gradient, Projected gradient descent},

pubstate = {published},

tppubtype = {inproceedings}

}