AI For Predictive Cybersecurity in Website Traffic Analysis

: The increasing sophistication of cyber threats necessitates advanced solutions for securing website traffic. This paper explores the integration of Artificial Intelligence (AI) in predictive analytics to enhance cybersecurity through real-time website traffic analysis. The proposed framework leverages AI-driven honeypots to attract and analyze malicious bots and cybercriminals, enabling the preemptive identification and mitigation of attack vectors. Implementing a Zero-Trust Architecture (ZTA) that verifies the identity of the users and devices involved continuously, based on their behavior and contextual factors, reduces the security gap even when the users may have successfully authenticated initially. The proposed study also develops an AI-powered system for the classification of URL safety, threat prediction, and efficient management of block/unblock actions, thus streamlining website security operations. Text message authentication further enhances the security framework by verifying user access and preventing unauthorized activities. This paper highlights the transformative potential of AI in predictive analytics, offering a comprehensive approach to cybersecurity in website traffic analysis.