Fuzzy ESVDF Approach for Intrusion Detection Systems

Intrusion Detection Systems (IDSs) deal with large amount of data containing irrelevant and redundant features, which leads to slow training and testing processes, heavy computational resources and low detection accuracy. Therefore, the features selection is an important issue in intrusion detection. Reducing the features set improves the system accuracy and speeds up the training and testing phases considerably. In this paper, we improve the Enhancing Support Vector Decision Function (ESVDF) approach by integrate it with a fuzzy inferencing model. The fuzzy inferencing model is used to accommodate the learning approximation and the small differences in the decision making steps of the ESVDF approach. It simplifies the design complexity and reduces the execution time of the ESVDF, which speeds up the features selection processing and facilitates any modification or changes in the features selection process that may happen later. In addition, it improves the overall performance of the ESVDF. We have examined the feasibility of our approach by conducting several experiments using the DARPA dataset. The experimental results indicate that the proposed algorithm can deliver a satisfactory performance in terms of classification accuracy, training and testing time.