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Conference Paper
Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System
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- Authors
- Joong-Hee Lee, Jong-Hyouk Lee, Seon-Gyoung Sohn, Jong-Ho Ryu, Tai-Myoung Chung
- Issue Date
- 2008-02
- Citation
- International Conference on Advanced Communication Technology (ICACT) 2008, pp.1170-1175
- Language
- English
- Type
- Conference Paper
- Project Code
- 07MK2200, The Development of Smart Monitoring and Tracing System against Cyber-attack in All-IP Network, Na Jung-Chan
- Abstract
- 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 Keywords
- 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