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

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

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıICC 2024 - IEEE International Conference on Communications
EditörlerMatthew Valenti, David Reed, Melissa Torres
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar2288-2293
Sayfa sayısı6
ISBN (Elektronik)9781728190549
DOI'lar
Yayın durumuYayınlandı - 2024
Etkinlik59th Annual IEEE International Conference on Communications, ICC 2024 - Denver, United States
Süre: 9 Haz 202413 Haz 2024

Yayın serisi

AdıIEEE International Conference on Communications
ISSN (Basılı)1550-3607

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???event.eventtypes.event.conference???59th Annual IEEE International Conference on Communications, ICC 2024
Ülke/BölgeUnited States
ŞehirDenver
Periyot9/06/2413/06/24

Bibliyografik not

Publisher Copyright:
© 2024 IEEE.

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