Hybrid Feature Selection for Network Intrusion
In computer communications, collecting and storing characteristics about connections into a data set is needed to analyze its behaviour. Generally this data set is multidimensional and larger in size. When this data set is used for classification it may end with wrong results and it may also occupy more resources especially in terms of time. Most of the features present are redundant and inconsistent and affect the classification. In order to improve the efficiency of classification these redundancy and inconsistency features must be eliminated.