Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System
| Autor: | Tai-Myoung Chung, Jong-Hyouk Lee, Jong-Ho Ryu, Seon-Gyoung Sohn, Joong-Hee Lee |
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| Rok vydání: | 2008 |
| Předmět: |
Training set
Process (engineering) business.industry Computer science Anomaly-based intrusion detection system Decision tree Intrusion detection system Machine learning computer.software_genre Set (abstract data type) Data set Anomaly detection Artificial intelligence Data mining business computer Test data |
| Zdroj: | 2008 10th International Conference on Advanced Communication Technology. |
| ISSN: | 1738-9445 |
| DOI: | 10.1109/icact.2008.4493974 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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