Robust Statistical Outlier Based Feature Selection Technique for Network Intrusion Detection

Provided by: International Journal of Soft Computing and Engineering (IJSCE)
Topic: Security
Format: PDF
For the last decade, it has become essential to evaluate machine learning techniques for web based intrusion detection on the KDD Cup 99 data set. Most of the computer security breaches cannot be prevented using access and data flow control techniques. This data set has served well to identify attacks using data mining. Furthermore, selecting the relevant set of attributes for data classification is one of the most significant problems in designing a reliable classifier. Existing C4.5 decision tree technology has a problem in their learning phase to detect automatic relevant attribute selection, while some statistical classification algorithms require the feature subset to be selected in a preprocessing phase.

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