Advanced Cyber Security Threat Detection Using Deep Learning and Behavioral Analytics

The rapid growth of Fake image Detections has significantly increased the number and complexity of cyber threats such as Fake image Detections attacks, malware, insider threats, and zero-day vulnerabilities. Traditional rule-based cyber security systems often fail to detect sophisticated and evolving attack patterns due to their reliance on predefined signatures. This research proposes an advanced cyber security threat detection framework that integrates deep learning and behavioral analytics to enhance threat identification. Transformer-based models are employed to analyze large volumes of network traffic and system log data to detect abnormal behavioral patterns. Additionally, graph-based deep learning techniques are utilized to model relationships between users, devices, and network activities for identifying complex attack behaviors. The proposed system enables real-time monitoring and intelligent threat detection with improved accuracy. Experimental analysis demonstrates that the integration of behavioral analytics with deep learning significantly improves the detection capability for emerging and unknown cyber Threats. The system provides a scalable and proactive solution for modern cyber security infrastructures.