NeuroSymbolic Intrusion Detection System

Abstract

This research introduces a hybrid neuro-symbolic framework designed to enhance network intrusion detection by integrating the predictive power of machine learning with the transparent logic of symbolic artificial intelligence. The escalating complexity of cybersecurity threats, particularly advanced persistent threats that exploit temporal vulnerabilities, necessitates a move beyond conventional black-box security tools. Our proposed system processes live network traffic through a modular pipeline that extracts both statistical and temporal features. These features are analyzed in parallel by two distinct machine learning models: a Random Forest classifier for recognizing established attack patterns and a Spiking Neural Network, inspired by biological processes, for detecting subtle, time-based anomalies. The outputs from these models are fused within an ensemble decision module, calibrated to optimize detection confidence and significantly reduce the incidence of false positives. A critical innovation of this architecture is its subsequent symbolic reasoning layer. Once a potential threat is identified with high confidence, this layer applies a rule-based logic to generate human-readable alerts. These alerts provide security analysts with a clear explanation of the decision, detailing the specific network events and features that triggered the warning. This moves beyond simple flagging to deliver actionable intelligence and justifiable reasoning. The system was rigorously evaluated on standard benchmark datasets, where it demonstrated superior performance compared to standalone model approaches, achieving higher detection accuracy and a more robust false-positive rate. The modular design ensures flexibility for real-world deployment across diverse enterprise environments. In conclusion, this work effectively bridges the critical gap between high-performance automated threat detection and the operational transparency required for effective cybersecurity defense.

Introduction

Modern enterprise networks face sophisticated cyber threats that often bypass traditional signature-based intrusion detection systems (IDS). A key challenge is the “black box” problem: AI-based IDS can detect attacks but cannot explain their reasoning, complicating forensics and response.

This study proposes a neuro-symbolic intrusion detection framework that combines:

• Random Forests for handling heterogeneous network data,

• Spiking Neural Networks (SNNs) to capture temporal and sequential attack patterns, and

• A symbolic reasoning layer for rule-based, human-understandable validation of alerts.

The system’s modular and scalable architecture supports both offline analysis and live-stream monitoring, while enabling easy integration of new detection modules and threat intelligence. The framework addresses the interpretability gap, providing accurate, explainable, and actionable alerts for security analysts.

Objectives include: feature extraction from packet- and session-level data, classifier training and optimization, ensemble decision-making to reduce false positives, and performance evaluation using benchmark intrusion detection datasets.

The proposed system’s workflow integrates live packet monitoring, classifier ensembles, symbolic validation, and an analyst-oriented dashboard. Experimental results show stable training of SNNs, improved detection accuracy, and clear classification performance demonstrated via confusion matrices and ROC curves.

Conclusion

The NeuroSymbolic IDS in this project shows how mergining advanced machine learning models (Random Forest and Spiking Neural Networks) with symbolic reasoning can be very effective. This hybrid architecture delivers strong cyber threat detection accuracy while supplying interpretable, actionable alerts that help security teams investigate and respond effectively. Comprehensive testing with the CICIDS-2017 dataset and in simulated live environments proved the system’s capability to identify diverse attack types, including novel or evasive threats, lowering false positive rates compared to standard approaches. The modular framework supports ongoing improvements, allowing rules and models to be updated easily as threats evolve, making it well-suited for enterprise deployments.

References

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Copyright

Copyright © 2025 Akhilesh H, Mr. Yadhukrishna M R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.