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학술대회 Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System
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저자
이중희, 이종혁, 손선경, 유종호, 정태명
발행일
200802
출처
International Conference on Advanced Communication Technology (ICACT) 2008, pp.1170-1175
DOI
https://dx.doi.org/10.1109/ICACT.2008.4493974
협약과제
07MK2200, AII-IP 환경의 지능형 사이버 공격 감시 및 추적 시스템 개발, 나중찬
초록
A decision tree is a outstanding method for the data mining. In intrusion detection systems (IDSs), the data mining techniques are useful to detect the attack especially in anomaly detection. For the decision tree, we use the DARPA 98 Lincoln Laboratory Evaluation Data Set (DARPA Set) as the training data set and the testing data set. KDD 99 Intrusion Detection data set is also based on the DARPA Set. These three entities are widely used in IDSs. Hence, we describe the total process to generate the decision tree learned from the DARPA Sets. In this paper, we also evaluate the effective value of the decision tree as the data mining method for the IDSs, and the DARPA Set as the learning data set for the decision trees.