International Journal of Advanced Research in Computer and Communication Engineering

Abstract: In today’s increasingly interconnected digital environment, protecting information systems from evolving cyber threats has become a critical necessity. Traditional intrusion detection and prevention systems often rely on static, rule-based approaches, which are insufficient to identify sophisticated and previously unseen attacks. This paper presents an Intelligent Intrusion Prevention System (IPS) that leverages machine learning techniques for advanced data protection and real-time threat analysis. The proposed system is deployed within a self-hosted Linux-based private cloud environment, created by converting a standard laptop into a secure server infrastructure, ensuring data sovereignty and administrative control. Machine learning models such as Random Forest, Support Vector Machine (SVM), and Deep Neural Networks (DNN) are employed to analyse network traffic and system logs, enabling accurate classification and prediction of intrusion activities. To enhance security, the system integrates encryption mechanisms, access control policies, and automated firewall actions, providing a multi-layered defence framework. A real-time monitoring dashboard visualizes intrusion attempts, system performance, and threat metrics, allowing prompt response and mitigation. The results demonstrate that the proposed system delivers an adaptive, scalable, and cost-effective cybersecurity solution capable of autonomous threat detection and prevention while preserving data privacy. This approach offers a practical and efficient framework suitable for academic, research, and small organizational environments.

Keywords: IPS, DNN, Real-Time Threat Analysis, Cybersecurity, Anomaly Detection.

Downloads: | DOI: 10.17148/IJARCCE.2026.15142