PREDICTIVE ANALYTICS FOR CYBER ATTACK DETECTION USING NEXT-GENERATION AI TECHNIQUES

Abstract

Next-generation AI is used to build a predictive analytics framework to identify cyber threats. Real-time simulation of complicated, high-dimensional security data streams uses graph neural networks, deep learning, and transformer topologies. Behavioral analytics and temporal sequence models can detect attack tendencies before they cause severe damage. Combining network data, system logs, and human actions from several places helps understand threats. Mixed learning enhances stability with sparsely labeled data by combining supervised, self-supervised, and reinforcement learning. Online education and idea control prepare people for any attack. Federated AI and privacy-preserving learning allow remote companies to work safely. Numerous tests using real-world and benchmark datasets have shown high recognition rates and low false alarm rates. The design protects against APTs, zero-day vulnerabilities, and malware that changes shape. Clearly explained AI modules alert. This helps security experts make rapid decisions. Distributed training and edge-cloud coupling enable fast responses, enabling scalability.