X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System

Kaya, Kiymet; Ak, Elif; Bas, Sumeyye; Canberk, Berk; Gunduz Oguducu, Sule

Abstract

The effectiveness of Intrusion Detection Systems (IDS) is critical in an era where cyber threats are becoming increasingly complex. Machine learning (ML) and deep learning (DL) models provide an efficient and accurate solution for identifying attacks and anomalies in computer networks. However, using ML and DL models in IDS has led to a trust deficit due to their non-transparent decision-making. This transparency gap in IDS research is significant, affecting confidence and accountability. To address, this paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural Networks (GNNs) to effectively process network traffic data, while also adapting a new Explainable AI (XAI) methodology. Unlike most GNN-based IDS that depend on labeled network traffic and node features, thereby overlooking critical packet-level information, our approach leverages a broader range of traffic data through network flows, including edge attributes, to improve detection capabilities and adapt to novel threats. Through empirical testing, we establish that our approach not only achieves high accuracy with 99.47% in threat detection but also advances the field by providing clear, actionable explanations of its analytical outcomes. This research also aims to bridge the current gap and facilitate the broader integration of ML/DL technologies in cybersecurity defenses by offering a local and global explainability solution that is both precise and interpretable.

Citation

Kaya, K., Ak, E., Bas, S., Canberk, B., & Gunduz Oguducu, S. (2024, June). X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System. Presented at ICC 2024 - IEEE International Conference on Communications, Denver, Colorado

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ICC 2024 - IEEE International Conference on Communications
• Start Date: Jun 9, 2024
• End Date: Jun 13, 2024
• Acceptance Date: Apr 3, 2023
• Publication Date: Jun 9, 2024
• Deposit Date: Oct 10, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 33
• Pages: 2288-2293
• Series ISSN: 1938-1883
• Book Title: IEEE International Conference on Communications (ICC) 2024
• ISBN: 9781728190556
• DOI: https://doi.org/10.1109/icc51166.2024.10622177
• Keywords: Index Terms-intrusion detection system; graph neural networks; ex- plainable artificial intelligence; self-supervised learning; edge embedding