Reconfigurable architectures for real-time network anomaly detection

A successful real-time intrusion detection is predicated on the ability to support the bandwidth demand of gigabit Ethernet. One portion of anomaly detection includes gathering additional network traffic information by extracting data based on network behavior. Those data are input to more advanced detection methods such as principal component analysis for anomaly detection. To achieve real-time performance, both the core algorithms and data gathering tools must satisfy bandwidth demands. In this Ph.D. work, we look to exploit reconfigurable hardware in a dynamic network environment and implement novel architectures for next generation intrusion detection systems utilizing anomaly detection schemes.