상세정보
학술대회
Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System
- 발행일
- 200802
- 출처
- International Conference on Advanced Communication Technology (ICACT) 2008, pp.1170-1175
- 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.
- KSP 제안 키워드
- Data mining(DM), Data sets, Decision Tree(DT), Detection data, Intrusion Detection Systems(IDSs), Intrusion detection system(IDS), KDD 99 intrusion detection, Laboratory evaluation, Learning data, Mining method, anomaly detection