AI-Powered Secure Network for Intelligent Threat Detection and Automated Mitigation Using Machine Learning

Author : A Ajina

Abstract : The rapid escalation of cyber threats has exposed critical limitations in traditional security mechanisms, which frequently fail to address evolving and sophisticated attack vectors. This research presents an AI-powered network security framework utilizing supervised machine learning algorithms specifically Random Forest and XGBoost for real-time vulnerability detection and automated threat mitigation. The architecture consists of three modular components: (i) a real-time traffic monitoring and AI-driven classification engine, (ii) a Node.js backend for automated mitigation and secure logging, and (iii) an interactive React.js dashboard for visualization and system control. Emphasizing scalability, modularity, and secure inter-component communication, the framework enables adaptive threat response via rule-based automation. Extensive experimental evaluation on benchmark intrusion detection datasets demonstrates a detection accuracy of 94.6% and a false positive rate below 6%, with robust performance under heavy network loads. Furthermore, the system establishes a foundation for integrating deep learning based anomaly detection, block chain enabled auditability, and cloud-native deployment. The results underscore the potential of AI-driven architectures to deliver proactive, scalable, and resilient cybersecurity solutions for contemporary digital ecosystems.

Keywords : Artificial Intelligence, Automated Mitigation, Intrusion Detection System (IDS), XGBoost.

Conference Name : International Conference on Robotics and Cybersecurity Integration (ICRCI-26)

Conference Place : Hyderabad, India

Conference Date : 3rd Jan 2026