Network Security White Papers
Decision Tree Classifier for Network Intrusion Detection With GA-Based Feature Selection
Overview Machine Learning techniques such as Genetic Algorithms and Decision Trees have been applied to the field of intrusion detection for more than a decade. Machine Learning techniques can learn normal and anomalous patterns from training data and generate classifiers that then are used to detect attacks on computer systems. In general, the input data to classifiers is in a high dimension feature space, but not all of features are relevant to the classes to be classified. This paper use a genetic algorithm to select a subset of input features for decision tree classifiers, with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection.
| Publisher | University of Central Florida | File Format | |
|---|---|---|---|
| Date Published | February 2005 | ||
| Format | White Papers | ||
| Topics | |||
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