A DATA-DRIVEN ANOMALY-BASED BEHAVIOR DETECTION METHOD FOR ADVANCED PERSISTENT THREATS

A DATA-DRIVEN ANOMALY-BASED BEHAVIOR DETECTION METHOD FOR ADVANCED PERSISTENT THREATS

ABSTRACT 

Advanced Persistent Threats (APTs) represent sophisticated and enduring network intrusion campaigns targeting sensitive information from targeted organizations and operating over a long period. These types of threats are much harder to detect using signature-based methods. Anomaly-based methods consist of monitoring system activity to determine whether an observed activity is normal or abnormal. This is done according to heuristic or statistical analysis and can be used to detect unknown attacks. Despite all significant research efforts, such techniques still suffer from a high number of false positive detections. Detecting APTs is complex because it tends to follow a “low and slow” attack profile that is very difficult to distinguish from normal, legitimate activity. The volume of data that must be analyzed is overwhelming. One technology that holds promise for detecting this kind of attack that is nearly invisible is big data analytics. In this work, I propose a data-driven anomaly-based behavior detection method that aims to leverage big data methods and is capable of processing significant amounts of data from diverse or several data sources. Big data analytics will significantly enhance or improve the detection capabilities, enabling the detection of Advanced Persistent Threats (APTs) activities that pass under the radar of traditional security solutions.