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 dynamic teams. The community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on the access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to form community structures based on the subjects accessed. CADS consist of two components: 1) relational pattern extraction, which derives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users have sufficiently 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 attacks such as viruses and spam. Traditional hardware solutions are intended for firewall routers. However, the solutions in the literature 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.