Download now Free registration required
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, the authors 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, they describe the total process to generate the decision tree learned from the DARPA Sets. In this paper, they 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.
- Format: PDF
- Size: 545.52 KB