대한원격탐사학회:학술대회논문집 (Proceedings of the KSRS Conference)
- 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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- Pages.301-304
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- 2005
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- 1226-9743(pISSN)
AN ANOMALY DETECTION METHOD BY ASSOCIATIVE CLASSIFICATION
- Lee, Bum-Ju (Datahase & Bioinformatics Laboratory, Chungbuk National University) ;
- Lee, Heon-Gyu (Datahase & Bioinformatics Laboratory, Chungbuk National University) ;
- Ryu, Keun-Ho (Datahase & Bioinformatics Laboratory, Chungbuk National University)
- 발행 : 2005.10.01
초록
For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques or frequent episode mining in order to analyze an audit data. But, since they mainly analyze the average behaviour of user's activities, some anomalies can be detected inaccurately. Therefore, we propose an anomaly detection method that utilizes an associative classification for modelling intrusion detection. Finally, we proof that a prediction model built from associative classification method yields better accuracy than a prediction model built from a traditional methods by experimental results.