LEVERAGING AI TO IDENTIFY ANOMALIES IN ELECTRICAL SYSTEMS AND COMMUNICATION NETWORKS, SAFEGUARDING CRITICAL INFRASTRUCTURE AGAINST CYBER-ATTACKS

Abstract

Every year the dependency on essential infrastructure systems keeps growing thus making them more vulnerable to complex cyber-attacks. The contemporary and multifaceted terrorist threats can rarely be handled effectively through conventional cybersecurity protocols. The paper evaluates how Artificial Intelligence (AI) coupled with Machine Learning (ML) performs forecasting and mitigation of security threats which affect critical infrastructure systems. Machine Learning together with Artificial Intelligence methods perform exceptionally well in real-time extensive data processing functions while discovering patterns that indicate cyber-attack occurrences. The research investigates sophisticated AI and ML algorithms including supervised learning and unsupervised learning as well as reinforcement learning because they demonstrate their ability to detect intrusions and assess vulnerabilities and predict threats. Primary analysis evaluates essential hurdles that involve AI model adversarial attacks as well as privacy concerns and data requirements for high-quality training samples. This research evaluates XAI as a key factor for enhancing transparency and reliability in security systems built using ML so they can be properly deployed in critical infrastructure environments. The paper evaluates this integration process between threat intelligence systems based on AI with present-day cyber frameworks while focusing on both real-time threat mitigation and flexible responses toward shifting attack routes. A.I.-driven problem recognition systems within Industrial Control Systems (ICS) demonstrate their ability to shorten operational stoppages while reducing financial damage and preventing infrastructure breakdowns according to a studied implementation. The outcomes demonstrate AI together with ML can boost critical infrastructure's cyber-attack resilience therefore establishing proactive cybersecurity practices instead of reactive ones. The study finishes by outlining projects for future research to create robust AI algorithms that defend against attacks along with standardized data accumulations for crucial infrastructure defense and strengthened cooperation between experts from different fields involving AI and cybersecurity specialists and managers of critical infrastructure systems. Vital infrastructure cybersecurity can achieve significant enhancement through the use of AI and ML technologies which will maintain key services dependable and safe and operational.