Towards Resilient Cyber Defense: Exploring the Synergy of Adversarial Robustness and Explainable AI in NIDS

DOI

10.2991/978-94-6463-740-3_8How to use a DOI?

Keywords

Cyber Security; Explainable AI; Adversarial Machine learning; Network Intrusion detection Systems

Abstract

Adversarial attacks affect the performance of NIDS as attackers subtly modify inputs to bypass detection in traditional machine learning models. Ensuring adversarial robustness is essential for maintaining reliable detection under such conditions. Explainable AI (XAI) provides transparent insights into NIDS decision-making, helping analysts respond effectively to incidents and adhere to compliance standards. However, the interplay between adversarial attacks and XAI remains underexplored, as manipulations can affect both detection accuracy and the trustworthiness of explanations, potentially leading to misinterpretations. This systematic review explores how adversarial robustness and Explainable AI (XAI) are applied within Network Intrusion Detection Systems (NIDS). It examines the different types of adversarial attacks, the vulnerabilities they exploit, and the methods used to carry them out. By analyzing several case studies that combine adversarial robustness with XAI, the review sheds light on the challenges and gaps in existing research. The paper also discusses the evaluation metrics commonly used to assess these approaches and suggests possible avenues for future research in the field.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chinu
AU  - Urvashi Bansal
PY  - 2025
DA  - 2025/06/25
TI  - Towards Resilient Cyber Defense: Exploring the Synergy of Adversarial Robustness and Explainable AI in NIDS
BT  - Proceedings of the 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)
PB  - Atlantis Press
SP  - 74
EP  - 86
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-740-3_8
DO  - 10.2991/978-94-6463-740-3_8
ID  - 2025
ER  -