Robust Intrusion Detection Systems with Explainable Artificial Intelligence: A Review
DOI:
https://doi.org/10.32628/CSEIT26123Abstract
With the recent growth of interconnected networks, Internet of Things (IoT), cyber-physical systems, and next-generation mobile communication networks, the complexities and dynamics of cybersecurity threats are rising at an unprecedented level. Intrusion Detection Systems (IDS) are important defense systems that detect harmful activities from the network traffic. Traditional approaches to IDS are faced with severe limitations regarding their high rates of false positives, low malleability to zero-day attacks, and low resistance to adversarial attacks. Recently, the use of advancements from Machine Learning (ML), Deep Learning (DL), and Explainable Artificial Intelligence (XAI) concepts gained significance to boost the resistance, interpretability, and trustworthiness aspects of IDS systems. Explainable AI allows security experts to better comprehend the rationale behind the IDS system’s predictions, observe anomalies, and optimize their response strategies accordingly. A thorough literature review is introduced, depicting the overall analysis of recent IDS systems integrated with the concept of Explainable AI, along with their algorithms, datasets, strengths, and weaknesses. A literature review analysis is introduced, followed by an embryonic approach on how IDS systems can become Explainable and Resistant.
Downloads
References
Kaushik, S., et al. (2025). Robust Machine Learning Based Intrusion Detection System Using Statistical Feature Selection. Scientific Reports. DOI: 10.1038/s41598-025-88286-9 DOI: https://doi.org/10.1038/s41598-025-88286-9
Abou El Houda, Z., et al. (2024). Securing Federated Learning through Blockchain and Explainable AI for Robust Intrusion Detection in IoT Networks. IEEE INFOCOM Workshops. DOI: 10.1109/INFOCOMWKSHPS61880.2024.10621234 DOI: https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10225769
Dhanushkodi, K., & Thejas, S. (2023). AI-Enabled Threat Detection: Leveraging Artificial Intelligence for Advanced Security and Cyber Threat Mitigation. IEEE Access. DOI: 10.1109/ACCESS.2023.3314876 DOI: https://doi.org/10.1109/ACCESS.2024.3493957
Ring, M., et al. (2019). A Survey of Network-Based Intrusion Detection Data Sets. Computers & Security. DOI: 10.1016/j.cose.2019.06.005 DOI: https://doi.org/10.1016/j.cose.2019.06.005
Kim, J., et al. (2020). CNN-Based Network Intrusion Detection against Denial-of-Service Attacks. Electronics. DOI: 10.3390/electronics9060916 DOI: https://doi.org/10.3390/electronics9060916
Moustafa, N., & Slay, J. (2015). UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems. MILCOM. DOI: 10.1109/MILCOM.2015.7348942 DOI: https://doi.org/10.1109/MilCIS.2015.7348942
Javaid, A., et al. (2016). A Deep Learning Approach for Network Intrusion Detection System. EAI Conference on Bio-inspired Information and Communications Technologies. DOI: 10.4108/eai.3-12-2015.2262516 DOI: https://doi.org/10.4108/eai.3-12-2015.2262516
Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS). DOI: 10.48550/arXiv.1705.07874
Kairouz, P., et al. (2021). Advances and Open Problems in Federated Learning. Foundations and Trends® in Machine Learning. DOI: 10.1561/2200000083 DOI: https://doi.org/10.1561/2200000083
Meng, Z., et al. (2021). Federated Learning-Based Intrusion Detection in IoT Networks. IEEE Internet of Things Journal. DOI: 10.1109/JIOT.2021.3059786
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
