A layered and integrative framework for Advance Persistent Threat detection and mitigation: combining AI, Zero-Trust, and Advanced Threat Intelligence

A layered and integrative framework for Advance Persistent Threat detection and mitigation: combining AI, Zero-Trust, and Advanced Threat Intelligence

연구 분야: Cryptography

학회: Cluster Computing

Advanced Persistent Threats (APTs) pose significant challenges to cybersecurity due to their sophisticated, stealthy, and persistent nature. This paper introduces a layered and integrative framework for detecting and mitigating APTs, combining cutting-edge technologies such as Artificial Intelligence (AI), Zero-Trust Architecture, and Advanced Threat Intelligence. The proposed framework leverages AI-driven behavioral analytics for early anomaly detection, integrating Zero-Trust principles to restrict lateral movement within networks. Additionally, it incorporates real-time threat intelligence to proactively identify and respond to emerging threats. By synergizing these technologies into a cohesive, multi-tiered approach, the framework addresses key vulnerabilities in modern cyber defense systems while enhancing both the detection and response phases of the APT lifecycle management. Experimental results demonstrate that the framework achieves a detection accuracy of 95.6%, a false positive rate of 2.3%, and a rapid mitigation response time of 1.4 seconds, significantly outperforming baseline systems. Case studies and simulations further show the framework’s effectiveness in reducing detection time, minimizing attack impact, and strengthening overall network resilience. This research underscores the importance of adaptive, multi-layered defenses in combating the evolving APT landscape and offers actionable insights for enterprises seeking robust cybersecurity solutions.