Machine Learning for APT Detection

Nowadays, countries face a multitude of electronic threats that have permeated almost all business sectors, be it private corporations or public institutions. Among these threats, advanced persistent (APTs) stand out as well-known example. APTs are highly sophisticated and stealthy computer network attacks meticulously designed to gain unauthorized access persist undetected within targeted networks for extended periods. They represent formidable cybersecurity challenge governments, corporations, individuals alike. Recognizing the gravity one most critical this study aims reach deeper understanding their nature propose multi-stage framework automated APT detection leveraging time series data. Unlike previous models, proposed approach has capability detect real-time based on stored attack scenarios. This conducts an extensive review existing research, identifying its strengths, weaknesses, opportunities improvement. Furthermore, standardized techniques been enhanced enhance effectiveness in detecting attacks. The learning process relies datasets sourced from various channels, including journal logs, traceability audits, systems monitoring statistics. Subsequently, efficient prevention system, known composition-based decision tree (CDT), developed operate complex environments. obtained results demonstrate consistently outperforms algorithms terms accuracy effectiveess.