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Uncertainty-Quantified Neurosymbolic AI for Open Set Recognition in Network Intrusion Detection
Conference proceeding

Uncertainty-Quantified Neurosymbolic AI for Open Set Recognition in Network Intrusion Detection

Jacob Sander, Chung-En Johnny Yu, Brian Jalaian and Nathaniel D. Bastian
MILCOM IEEE Military Communications Conference, pp.13-18
MILCOM 2024 (Washington, DC, USA, 10/28/2024–11/01/2024)
10/28/2024
DOI: https://doi.org/10.1109/MILCOM61039.2024.10773953
Web of Science ID: WOS:001419571100053

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3 Times Cited - Web of Science

Abstract

Network Intrusion Detection Systems (NIDS) are crucial for safeguarding networks by detecting and classifying malicious traffic in real-time. This paper presents a novel neurosymbolic artificial intelligence (NSAI) approach for enhancing NIDS which we call ODXU, which combines neural networks with symbolic reasoning to improve classification accuracy, particularly for challenging classes. Additionally, we introduce uncertainty quantification techniques-Confidence Scoring, Shannon Entropy, and post-hoc Uncertainty Metamodeling-to enhance the reliability of the NIDS. Our experimental results demonstrate that our NSAI model, coupled with post-hoc Uncertainty Meta-modeling, outperforms traditional methods, providing superior detection accuracy and robust uncertainty estimates.

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