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Neuro-Symbolic Integration for Open Set Recognition in Network Intrusion Detection
Conference proceeding   Peer reviewed

Neuro-Symbolic Integration for Open Set Recognition in Network Intrusion Detection

Alice Bizzarri, Chung-En Yu, Brian Jalaian, Fabrizio Riguzzi and Nathaniel D. Bastian
AIxIA 2024 – Advances in Artificial Intelligence, Vol.15450, pp.50-63
Lecture Notes in Artificial Intelligence
International Conference of the Italian Association for Artificial Intelligence, XXIIIrd (Bolzano, Italy, 11/25/2024–11/28/2024)
01/01/2025
DOI: https://doi.org/10.1007/978-3-031-80607-0_5
Web of Science ID: WOS:001532132700005

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Abstract

Open Set Recognition (OSR) addresses the challenge of classifying inputs into known and unknown categories, a crucial task where labeling is often prohibitively expensive or incomplete. This is particularly vital in applications like Network Intrusion Detection Systems (NIDS), where OSR is used to identify novel, previously unknown attacks. We propose a neuro-symbolic integration approach that combines deep learning and symbolic methods, enhancing deep embedding for clustering with custom loss functions and leveraging XGBoost's decision tree algorithms. Our methodology not only robustly addresses the identification of previously unknown attacks in NIDS but also effectively manages scenarios involving covariance shift. We demonstrate the efficacy of our approach through extensive experimentation, achieving an AUROC of 0.99 in both contexts. This paper presents a significant step forward in OSR for network intrusion detection by integrating deep and symbolic learning to handle unforeseen challenges in dynamic environments.

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