AI-Enhanced Intrusion Detection Systems for Strengthening Critical Infrastructure Security

The innovation in the complex attacks continues to pose a risk to critical infrastructure systems, hence the need to exert enhanced security. Intrusion Detection Systems (IDS), which are typically based on static positive signatures and rule-based technologies, is incapable of identifying new threats and tends to produce more false alarms and slow reaction times. Thus, this research introduces an ML-integrated IDS that incorporates the use of the state-of-art ML techniques, including neural networks, support vector machines, reinforcement learning, among others, to enhance ID accuracy, reduce false alarms, and minimize detection delay. In the evaluated study, simulated in the critical infrastructure environment and evaluated by IDS using NSL-KDD dataset the AI-IDS had significant performances: 95% detection accuracy, 4% false positive and average of 0.8 sec of detection latency. The system displayed better flexibility and effectiveness for threat detection and response actions while offering improved performance in real-time arrangements. These changes over the traditional approaches show great potential in propelling better protection of critical assets against new and advanced cyber threats.