HYBRID NETWORK INTRUSION DETECTION SYSTEM BASED ON SIGNATURE ANALYSIS AND MACHINE LEARNING METHODS

Modern network infrastructures face escalating security threats due to the increasing complexity of cyberattacks. Traditional signature-based intrusion detection systems struggle to identify novel, zero-day anomalies, while pure machine learning approaches often suffer from high false-positive rates. To address these limitations, this research project explores a hybrid network intrusion detection system that integrates traditional signature-based analysis with advanced machine learning methods. The proposed architecture combines the deterministic accuracy of signature matching for known threats with the generalization capabilities of a neural network model for anomaly detection. Through multiclass classification of network traffic, the hybrid system aims to expand attack detection coverage and significantly reduce false-positive rates. The methodology involves analyzing system

requirements, designing a unified hybrid architecture, and conducting experimental simulations using standard network traffic datasets. This project provides comprehensive implementation support and research direction for evaluating the performance of the hybrid model against standalone detection mechanisms. The findings substantiate the theoretical and practical viability of deploying such hybrid frameworks within corporate network environments to enhance overall cybersecurity posture.

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