An Approach to detect Advanced Persistent Threats using Machine Learning Techniques

Abstract

Advanced Persistent Threats (APTs) pose significant risks to organizations due to their stealthy, prolonged nature and ability to evade traditional detection mechanisms. Traditional solutions often analyze separate data elements, such as network traffic or endpoint activity, limiting their effectiveness against sophisticated APT campaigns. This research proposes a holistic machine learning (ML)-driven approach to detect APTs by integrating three critical data dimensions: user behavior anomalies, endpoint activity monitoring, and network traffic analysis. The system further incorporates Tactics, Techniques, and Procedures (TTP) analysis using the MITRE ATT&CK framework to provide actionable intelligence. A real-time dashboard visualizes the threat detection results, TTP mappings, and mitigation strategies, enabling cybersecurity teams to respond proactively. The integration of multiple ML models enhances detection accuracy while bridging the gap between threat identification and contextual understanding. Experimental validation demonstrates the system's capability to detect APT indicators across diverse attack vectors and prioritize high-risk TTPs. This work contributes to advancing APT detection methodologies by offering a scalable, multi-dimensional solution tailored for modern cybersecurity operations.