Hybrid Intrusion Detection System for Enhanced SME Network Security
DOI:
https://doi.org/10.15662/IJEETR.2026.0802039Keywords:
Hybrid Intrusion Detection System, Artificial Intelligence, Machine Learning, XGBoost, Network Security, SME Cybersecurity, Anomaly Detection, Adaptive Learning, Cyber Threats, Intelligent Security SystemsAbstract
The rapid increase in the complexity and volume of cyber threats has created an urgent need for intelligent and adaptive Intrusion Detection Systems (IDS) to secure modern digital infrastructures, particularly within Small and Medium Enterprises (SMEs). Conventional IDS mechanisms, primarily based on signature matching and static rule sets, are inadequate for identifying zero-day attacks and evolving intrusion patterns. To overcome these limitations, Artificial Intelligence (AI) has emerged as a powerful enhancement for IDS frameworks. AI-driven IDS integrate Machine Learning (ML) techniques to analyze large-scale network traffic, system logs, and user behavior data, enabling accurate detection of anomalous and malicious activities
The rapid increase in the complexity and volume of cyber threats has created an urgent need for intelligent and adaptive Intrusion Detection Systems (IDS) to secure modern digital infrastructures, particularly within Small and Medium Enterprises (SMEs). Conventional IDS mechanisms, primarily based on signature matching and static rule sets, are inadequate for identifying zero-day attacks and evolving intrusion patterns. To overcome these limitations, Artificial Intelligence (AI) has emerged as a powerful enhancement for IDS frameworks. AI-driven IDS integrate Machine Learning (ML) techniques to analyze large-scale network traffic, system logs, and user behavior data, enabling accurate detection of anomalous and malicious activities
This paper presents a hybrid AI-driven intrusion detection framework designed for SME network environments. The proposed system leverages XGBoost-based classification to improve detection accuracy while maintaining computational efficiency. Experimental evaluation demonstrates superior performance compared to conventional IDS approaches.
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