Real-Time Network Intrusion Detection Using Convolutional Neural Networks and Behavioral Analytics

Abstract

The rapid expansion of interconnected digital systems has significantly increased exposure to cyber threats, making traditional security mechanisms insufficient for modern network protection. Network Intrusion Detection Systems (NIDS) are essential for identifying malicious activities; however, conventional approaches struggle with scalability, real-time processing, and detection of previously unseen attacks. This study presents a real-time intrusion detection framework that integrates Convolutional Neural Networks (CNNs) with behavioral analytics to enhance detection accuracy, adaptability, and responsiveness. The CNN component is employed for automated feature extraction from raw network traffic, while behavioral analytics models’ user and system activity patterns to detect anomalies. The hybrid system combines deep learning-driven classification with behavioral deviation analysis to improve detection of both known and unknown attacks. Experimental evaluation demonstrates that the proposed model achieves high accuracy, improved precision-recall balance, reduced false positives, and efficient real-time performance, making it suitable for deployment in modern high-speed network environments.

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