Combating advanced persistent threats: From network event correlation to incident detection
An advanced persistent threat (also known as APT) is a deliberately slow-moving cyberattack that is applied to quietly compromise interconnected information systems without revealing itself. APTs often use a variety of attack methods to get unauthorized system access initially and then gradually spread throughout the network. In contrast to traditional attacks, they are not used to interrupt services but primarily to steal intellectual property, sensitive internal business and legal documents and other data. If an attack on a system is successful, timely detection is of paramount importance to mitigate its impact and prohibit APTs from further spreading. However, recent security incidents, such as Operation Shady Rat, Operation Red October or the discovery of MiniDuke  just to name a few  have impressively demonstrated that current security mechanisms are mostly insufficient to prohibit targeted and customized attacks. This paper therefore proposes a novel anomaly detection approach which is a promising basis for modern intrusion detection systems. In contrast to other common approaches, which apply a kind of black-list approach and consider only actions and behaviour that match to well-known attack patterns and signatures of malware traces, our system works with a white-list approach. Our anomaly detection technique keeps track of system events, their dependencies and occurrences, and thus learns the normal system behaviour over time and reports all actions that differ from the created system model. In this work, we describe this system in theory and show evaluation results from a pilot study under real-world conditions.
advanced persistent threat event correlation anomaly detection intrusion detection log file analysis self-learning system model