AI-Augmented Incident Response Strategies for Real-Time Detection, Containment, and Mitigation of Multi-Stage Cyber Attacks in Large-Scale Critical Infrastructures
Keywords:
AI-driven cybersecurity, multi-stage cyber-attacks, incident response, real-time mitigation, machine learning, critical infrastructure securityAbstract
The increasing sophistication of cyber-attacks against critical infrastructure requires real-time, AI-driven incident response strategies to ensure rapid detection, containment, and mitigation. Traditional security methods struggle to cope with multi-stage attacks that evolve dynamically. AI-driven approaches, leveraging machine learning (ML), deep learning (DL), and threat intelligence, enhance response capabilities, reducing damage and downtime. This paper explores AI-augmented methodologies, focusing on predictive analytics, automated defense mechanisms, and anomaly detection. It also discusses past research, practical implementations, and emerging trends.
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