A new intrusion detection system using support vector machines and hierarchical clustering

Whenever an intrusion occurs, the security and value of a computer system is compromised. Network-based attacks make it difficult for legitimate users to access various network services by purposely occupying or sabotaging network resources and services. This can be done by sending large amounts of network traffic, exploiting well-known faults in networking services, and by overloading network hosts. Intrusion Detection attempts to detect computer attacks by examining various data records observed in processes on the network and it is split into two groups, anomaly detection systems and misuse detection systems. Anomaly detection is an attempt to search for malicious behavior that deviates from established normal patterns. Misuse detection is used to identify intrusions that match known attack scenarios. Our interest here is in anomaly detection and our proposed method is a scalable solution for detecting networkbased anomalies. We use Support Vector Machines (SVM) for classification. ...

• Type: Conference
• Year: 2007
• Where: VLDB
• Authors: Latifur Khan, Mamoun Awad, Bhavani M. Thuraisingham
• Keywords: Database, Hierarchical Agglomerative Clustering, Hierarchical Clustering Algorithms, Hierarchical Clustering Analysis, VLDB 2007