Development of real-time threat detection systems with AI-driven cybersecurity in critical infrastructure

Abstract

Protection of infrastructure is becoming increasingly demanding, and the sophistication and severity of cyber threats are increasing daily. Traditional threat detection techniques cannot match the ever-evolving nature of cyber threats, which increases the number of false positives and attack misses. AI-driven methods address these shortfalls via the use of advanced learning algorithms to detect and respond to newly discovered threats in real time. They are largely static rule-based or signature-based attacks, and they do not perform effectively against zero-day attacks and highly organized, advanced attacks. Given the critical need to protect digital infrastructures such as energy, transport, and communications from destruction, which threatens security and operational integrity, an adaptive means for real-time and accurate threat detection must evolve. This research aims to determine the optimum method for designing and testing an AI-based real-time threat detection system that is suitable for use in critical infrastructure environments. Compared with traditional methods, the proposed system uses an advanced machine learning technique to provide better detection accuracy, adaptiveness, and efficiency of results. It is designed to integrate all the critical features of data integration, anomaly detection, and feature extraction along with an automated response mechanism that allows the system to detect various types of threats and cyberattacks, including new and sophisticated ones, without much human intervention. Some of the key performance indicators, including accuracy, precision, recall, and F1 score, ensure that, indeed, the system is effective. The research findings illustrate that for clear readability, the AI-based detection system reported an accuracy value of 0.95, where precision is 0.93 and a recall value of 0.92 with the F1 score of 0.92, hence performing better than do conventional methods of threat detection. This suggests that it reports a high rate of false-positive rejection while returning proper alerts in the case of real-time operation. This was also enhanced by an automated response feature of the system that provided faster threat mitigations with shorter times for all types of responses, leading to even improved security. Finally, the paper has demonstrated how the AI-based approach is a viable and scalable solution towards mitigating current cybersecurity challenges in critical infrastructures and, at the same time, providing opportunities for further research into more robust, flexible, and autonomous defense systems.