Data Mining in Security Applications

Abstract

In recent years, research in many security areas has gained a lot of interest among scientists in academia, industry, military and governmental organizations. Researchers have been investigating many advanced technologies to effectively solve acute security problems. Data mining has certainly been one of the most explored technologies successfully applied in many security applications ranging from computer and physical security and intrusion detection to cyber terrorism and homeland security. For example, in the context of homeland security, data mining can be a potential means to identify terrorist activities, such as money transfers and communications, and to identify and track individual terrorists themselves, such as through travel and immigration records (Seifert, 2007). In another data mining’s success story related to security, credit card fraud detection, all major credit card companies mine their transaction databases, looking for spending patterns that indicate a stolen card. In addition, data mining has also effectively been utilized in many physical security systems (e.g. in efficient system design tools, sensor fusion for false alarm reduction) and video surveillance applications, where many data mining based algorithms have been proposed to detect motion or intruder at monitored sites or to detect suspicious trajectories at public places. This chapter provides an overview of current status of data mining based research in several security applications including cyber security and intrusion detection, physical security and video surveillance.