A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
Aggregation and Correlation of Intrusion-Detection Alerts
RAID '00 Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection
Alert Correlation in a Cooperative Intrusion Detection Framework
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Mining Alarm Clusters to Improve Alarm Handling Efficiency
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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The existing well-known network based intrusion detection / prevention techniques such as the misuse detection technique, etc., are widely used. However, because the misuse detection based intrusion prevention system is proportionally depending on the detection rules, it causes excessive large false alarm which is linked to wrong correspondence. This study suggests an intrusion prevention system which uses multi-class Support Vector Machines(SVM) as one of the rule based intrusion prevention system and anomaly detection system in order to solve these problems. When proposed scheme is compared with existing intrusion prevention system, it show enhanced performance result that improve about 20% and propose false positive minimize with effective detection on new variant attacks.