International Journal of Advanced Research in Computer and Communication Engineering

Abstract: In recent years, the rapid growth of interconnected digital systems has significantly increased the number and complexity of cyber attacks. Security Information and Event Management (SIEM) systems play an important role in monitoring and analyzing security events in modern organizations. However, traditional SIEM platforms rely mainly on static rule-based detection and manual analysis, which limits their ability to detect unknown threats and respond efficiently in real time. This paper proposes an AI-driven SIEM system for real-time cyber threat detection and security monitoring. The proposed system integrates network traffic analysis, system performance monitoring, and deep learning–based intrusion detection to provide intelligent and automated security analysis. A Convolutional Neural Network is used to classify network behavior as normal or suspicious, while an AI-based alert interpretation module generates concise, human-readable security summaries. The system also monitors CPU usage, memory consumption, and disk activity to provide holistic situational awareness. Experimental results show that the proposed system improves detection accuracy and reduces false alerts compared to conventional SIEM approaches. The developed framework offers an effective and scalable solution for modern cybersecurity environments.

Keywords: Security Information and Event Management, Intrusion Detection, Artificial Intelligence, Deep Learning, Cybersecurity, Real-Time Monitoring.

Downloads: | DOI: 10.17148/IJARCCE.2026.151120