Mutual clustered redundancy assisted feature selection for an intrusion detection system

Intrusion Detection is very important in computer networks because the widespread of internet makes the computers more prone to several cyber-attacks. With this inspiration, a new paradigm called Intrusion Detection System (IDS) has emerged and attained a huge research interest. However, the major challenge in IDS is the presence of redundant and duplicate information that causes a serious computational problem in network traffic classifications. To solve this problem, in this paper, we propose a novel IDS model based on statistical processing techniques and machine learning algorithms. The machine learning algorithms incudes Fuzzy C-means and Support Vector Machine while the statistical processing techniques includes correlation and Joint Entropy. The main purpose of FCM is to cluster the train data and SVM is to classify the traffic connections. Next, the main purpose of correlation is to discover and remove the duplicate connections from every cluster while the Joint entropy is applied for the discovery and removal of duplicate features from every connection. For experimental validation, totally three standard datasets namely KDD Cup 99, NSL-KDD and Kyoto2006+ are considered and the performance is measured through Detection Rate, Precision, F-Score, and accuracy. A five-fold cross validation is done on every dataset by changing the traffic and the obtained average performance is compared with existing methods.