Analysis of Dimensionality Reduction in Intrusion Detection

Intrusion detection system is an important technology in the market sector as well as in the area of research. Intrusion detection is considered a useful security tool that assists in preventing attacker’s access to networks or systems. The determination of genuineness of packets is a key issue and various approaches of classification have been presented. The complexity of a classifier is greatly reduced if the numbers of attributes in a data set are reduced. Analysis of dimensionality reduction and it is impacting thereof is the objective of our study. An experimental study is carried out to build up a classifier on a standard dataset of network traffic data that includes normal packets and abnormal packets. A rough set theory and information gain approaches are employed to reduce dimensionality of network traffic data set. The features obtained by the rough set theory and information gain are used to train and test the J48 classifier. A comparative analysis of the results obtained a reduced attribute set and original attributes are presented. The results shows that the performance of J48 classifier with the reduced attributes (rough set and information gain) is better, which is at the cost of time KeywordIntrusion Detection System, Rough Set Theory, Information Gain, J48 Classifier