AI ASSISTED END-TO-END ARCHITECTURE FOR DETECTING PERSISTENT ATTACKS IN ENTERPRISE NETWORKS

Abstract

AI-Assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks proposes an intelligent and comprehensive cybersecurity framework designed to identify and mitigate advanced persistent threats (APTs) within complex enterprise environments. Persistent attacks are highly sophisticated, stealthy, and long-term cyber intrusions that evade traditional security mechanisms by exploiting system vulnerabilities and maintaining unauthorized access over extended periods. This project introduces an integrated architecture that leverages artificial intelligence and machine learning techniques to enhance threat detection, behavioral analysis, and response automation. The system combines data from multiple sources, including network traffic, system logs, user activity, and endpoint sensors, to create a unified security monitoring platform. Advanced algorithms such as anomaly detection models, deep learning networks, and graph-based analysis are employed to identify unusual patterns and hidden attack behaviors. The architecture also incorporates real-time threat intelligence, feature extraction, and correlation engines to improve detection accuracy and reduce false positives. Additionally, automated response mechanisms are integrated to isolate compromised systems and trigger alerts, ensuring rapid incident handling. The proposed solution supports scalability, adaptability, and continuous learning, enabling it to evolve with emerging cyber threats. Experimental results indicate that the system significantly improves detection rates compared to traditional intrusion detection systems while minimizing response time. Overall, this research contributes to the development of robust, intelligent, and proactive cybersecurity solutions capable of protecting enterprise networks from sophisticated and persistent cyberattacks.