AI-Driven Threat Intelligence for Proactive Cyberattack Detection and Response: A Machine Learning Approach for Modern Security Ecosystems
Keywords:
Anomaly detection, Artificial intelligence, Cyberattack prediction, Cybersecurity, Intrusion detection, Machine learning, Network security, Threat intelligenceAbstract
Cyberattacks are increasing in scale, frequency, and sophistication, posing major risks to individuals, organizations, and governments. Traditional signature-based security mechanisms fail to detect novel attacks and cannot rapidly adapt to evolving threats. This study proposes an AI-driven threat intelligence framework that integrates machine learning models for proactive cyberattack detection and automated response. Network traffic, system logs, and threat intelligence feeds are processed to extract behavioural patterns, enabling early detection of anomalies and Indicators of Compromise (IoCs). Experimental simulations demonstrate that the proposed model improves detection accuracy and reduces false positives compared to baseline intrusion detection methods. The research highlights the potential of AI to strengthen cybersecurity ecosystems by enabling predictive and adaptive defences against modern cyber threats.
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