Advanced persistent threat detection through multi-modal behavioral analysis

Advanced Persistent Threats (APTs) represent sophisticated cyberattacks characterized by stealth, persistence, and evasion of traditional detection mechanisms. We observed that APT behaviors during lateral movement and data exfiltration share notable similarities with insider threat activities, leading us to explore cross-domain learning opportunities. This paper introduces a novel machine learning approach leveraging the CERT Insider Threat Dataset to simulate and detect APT behaviors through AI-augmented analytics. Our methodology integrates multi-modal data analysis, language model-driven behavioral understanding, and advanced machine learning to create realistic APT simulations from insider threat data. We developed three key technical components: a multi-agent language model architecture for log analysis, temporal sequence modeling for behavioral pattern recognition, and deep evidential clustering for uncertainty-aware threat detection that reduces false positives. Our research contributes four advances: a novel methodology for simulating APT patterns using insider threat data, an AI-enhanced multi-modal approach processing structured logs and communications, superior performance compared to existing methods, and practical deployment guidelines for enterprise environments. Experimental results achieved 96.3% detection accuracy while reducing false positives by 42% compared to state-of-the-art methods. Our system successfully simulates realistic APT scenarios across attack stages while providing interpretable explanations through natural language generation. The integration of large language models enables sophisticated analysis of unstructured data sources, offering contextual understanding beyond traditional approaches. This research addresses a critical gap for organizations seeking enhanced APT detection without extensive APT-specific training data. Our approach’s ability to learn from insider threat patterns while maintaining high accuracy makes it valuable for enterprise security operations and threat hunting teams facing resource constraints.