Hybrid Classifier Systems for Intrusion Detection

Authors:
Te-Shun Chou;Tsung-Nan Chou
Affiliations:
-;-
Venue:
CNSR '09 Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference
Year:
2009

Citing 5
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Abstract

This paper describes a hybrid design for intrusion detection that combines anomaly detection with misuse detection. The proposed method includes an ensemble feature selecting classifier and a data mining classifier. The former consists of four classifiers using different sets of features and each of them employs a machine learning algorithm named fuzzy belief k-NN classification algorithm. The latter applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The outputs of ensemble feature selecting classifier and data mining classifier are then fused together to get the final decision. The experimental results indicate that hybrid approach effectively generates a more accurate intrusion detection model on detecting both normal usages and malicious activities.