期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2013
卷号：2
期号：9
页码：2799-2802
出版社：IJECS
摘要：Collaborative information systems (CISs) are deployed within a diverse array of environments that manage sensitive information.Current security mechanisms detect insider threats, but they are ill-suited to monitor systems in which users function in dynamicteams. The community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based onthe access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to formcommunity structures based on the subjects accessed. CADS consist of two components: 1) relational pattern extraction, whichderives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users havesufficiently deviated from communities. We further extend CADS into Meta CADS to account for the semantics of subjects.Network security applications generally require the ability to perform powerful pattern matching to protect against attackssuch as viruses and spam. Traditional hardware solutions are intended for firewall routers. However, the solutions in theliterature for firewalls are not scalable, and they do not address the difficulty of an antivirus with an ever-larger pattern set.Related works have focused on algorithms and have even developed specialized circuits to increase the scanning speed
关键词：Common Information System; Community anomaly Detection
