AI-Driven Cyber Threat Detection and Response
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I1P116Keywords:
Artificial Intelligence, Cybersecurity, Threat Detection, Incident Response, Anomaly Detection, Behavioral Analytics, Intrusion Detection Systems, Security Automation, Adversarial Machine Learning, Zero-Day Attacks, Cyber ResilienceAbstract
The rapid digitization of modern society has dramatically expanded the scale, complexity, and sophistication of cyber threats. Traditional cybersecurity mechanisms, which rely heavily on static rules, signature-based detection, and manual analysis, struggle to keep pace with evolving attack vectors such as advanced persistent threats, zero-day exploits, ransomware campaigns, and coordinated distributed denial-of-service attacks. Artificial intelligence has emerged as a transformative force in cybersecurity, enabling automated, adaptive, and real-time threat detection and response. AI-driven cyber defense systems leverage machine learning, deep learning, anomaly detection, behavioral analytics, and automated decision-making frameworks to identify malicious activities, predict potential vulnerabilities, and orchestrate rapid mitigation strategies. By analyzing vast volumes of network traffic, user behavior patterns, system logs, and endpoint data, AI systems can detect subtle indicators of compromise that would otherwise evade conventional defenses. This article provides a comprehensive and in-depth exploration of AI-driven cyber threat detection and response, examining foundational technologies, system architectures, detection methodologies, response automation, adversarial challenges, ethical considerations, and future research directions. It highlights how intelligent cybersecurity systems enhance resilience, reduce response times, and enable proactive defense strategies in an increasingly interconnected and high-risk digital environment.
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