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A Hybrid Intrusion Detection System Integrating Machine Learning and Signature-Based Techniques for Enhanced Network Security

Authors:
Ayobola Victor Amubioya

Addresses:
Department of Computer and Information Sciences, Northumbria University, Newcastle Upon Tyne, England, United Kingdom.

Abstract:

The fast growth of internet connectivity has made digital infrastructures, like enterprise networks, cloud platforms, and Internet of Things (IoT) environments, much more likely to be targeted by advanced cyber-attacks. As the threat landscape worsens, researchers need to develop advanced security systems capable of detecting both known and emerging threats. To address this issue, this study suggests a Hybrid Intrusion Detection System (IDS) that combines machine learning with traditional signature-based detection methods. The proposed system uses benchmark network datasets that have been thoroughly pre-processed to improve data quality and feature relevance. Then, different machine learning algorithms are used to model and predict anomalous network behavior that could indicate a security breach. These predictive outcomes are then used with signature-based detection to cross-validate the results, which makes detection more reliable. The hybrid approach aims to leverage the best of both methods: machine learning's ability to adapt to new threats and signature-based systems' ability to detect known attack patterns. The proposed Hybrid IDS significantly improves detection rates while lowering false positives compared to traditional intrusion detection systems, as shown by experimental results. Also, the system remains computationally efficient, making it well-suited for real-world, dynamic, large-scale network settings.

Keywords: Internet of Things; Intrusion Detection System; Internet Connectivity; Machine Learning; Digital Ecosystems; Cyber Attacks; Anomaly Detection; Signature-Based Detection; Batch Intrusion Analysis.

Received on: 28/05/2025, Revised on: 03/08/2025, Accepted on: 04/10/2025, Published on: 07/03/2026

DOI: 10.69888/FTSIS.2026.000666

FMDB Transactions on Sustainable Intelligence and Security, 2026 Vol. 1 No. 1, Pages: 29-52

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