ANALYSIS OF ATTACK DETECTION METHODS ON CRITICAL INFRASTRUCTURE BASED ON BEHAVIORAL ANOMALIES

The increasing digitization of critical infrastructure systems—such as energy, water, transportation, and finance—has significantly expanded the surface for cyberattacks. Traditional signature-based intrusion detection methods are no longer sufficient to address advanced persistent threats and novel attack vectors that exploit behavioral weaknesses. This article presents a comprehensive analysis of behavioral anomaly detection methods as a proactive approach to securing critical infrastructure. By evaluating statistical, machine learning, and hybrid models using benchmark datasets like CICIDS2017, SWaT, and NSL-KDD, the study identifies key strengths and limitations of current techniques. It highlights the importance of context-aware modeling, real-time monitoring, and human behavior integration to reduce false positives and improve detection accuracy. The paper further explores emerging technologies, including federated learning and explainable AI, that enhance the adaptability and transparency of detection systems. Findings suggest that a holistic, behavior-focused strategy is essential for enhancing cyber resilience and mitigating risks across critical infrastructure sectors.