Selection of Effective Network Parameters in Attacks for Intrusion Detection

Current Intrusion Detection Systems (IDS) examine a large number of data features to detect intrusion or misuse patterns. Some of the features may be redundant or with a little contribution to the detection process. The purpose of this study is to identify important input features in building an IDS that are computationally efficient and effective. This paper proposes and investigates a selection of effective network parameters for detecting network intrusions that are extracted from Tcpclump DARPA 1998 dataset. Here PCA method is used to determine an optimal feature set. An appropriate feature set helps to build efficient decision model as well as to reduce the population of the feature set. Feature reduction will speed up the training and the testing process for the attack identification system considerably. Tcpclump of DARPA 1998 intrusion dataset was used in the experiments as the test data. Experimental results indicate a reduction in training and testing time while maintaining the detection accuracy within tolerable range.