AI-Driven Cyber Threat Detection and Log Analysis

Cyber threats are evolving into more complex attacks, which put digital infrastructure at serious risk. To defend against such threats, AI-based cyber threat detection systems utilize log analysis to identify malicious behavior. The system analyzes system logs from diverse sources including network traffic, authentication logs, and application logs. The system then identifies suspicious patterns and anomalies. Machine learning models are complemented with rule-based detection and statistical techniques to provide higher detection accuracy and low false positives. Real-time monitoring and automated response mechanisms allow proactive defense against threats, minimizing the effects of cyberattacks. The integration of Security Information and Event Management (SIEM) tools supports centralized log gathering, automated notifications, and sophisticated threat correlation. Supervised learning methods support the detection of malicious activities like malware spreading, intrusion attempts, and Distributed Denial-of-Service (DDoS) attacks. Feature selection techniques such as SHAP analysis enhance model efficiency by determining important factors that drive attack predictions. The adaptive methodology keeps organizations ahead of new cyber threats with smart security mechanisms. Through the integration of AI, machine learning, and SIEM, the system enhances threat detection, incident response, and forensic analysis.