Identification of Effective Optimal Network Feature Set for Probing Attack Detection Using PCA Method

Existing intrusion detection techniques emphasize on building intrusion detection model based on all features provided. But not all the features are relevant ones and some of them are redundant and useless. This paper proposes and investigates identification of effective network features for Probing attack detection using PCA method to determine an optimal feature set. An appropriate feature set helps to build efficient decision model as well as a reduced feature set. Feature reduction will speed up the training and the testing process considerably. This paper proposes a strategy to focus on intrusion detection involving statistical analysis of both attack and normal traffics based on the DARPA 1998 dataset as the training data. DARPA 1998 dataset was also used in the experiments as the test data. Experimental results show a reduction in training and testing time while maintaining the detection accuracy within acceptable range.