An Competent Intrusion Detection System using Relevance Vector Machine

Nowadays everyone needs Internet for their useful. So internet becomes a public network in the world wide. Intrusion Detection (ID) is the art of detecting inappropriate, incorrect, or anomalous activity. It is a security service that monitors and analyzes system events for the idea of finding, and providing near real-time or realtime notice of, attempts in order to access system resources in an unauthorized manner In proposed system the `relevance vector machine' (RVM), a model of same functional form to the popular and state-of-the-art `support vector machine' (SVM). It demonstrate that by exploiting a probabilistic Bayesian learning framework and it can derive accurate prediction models which typically utilize dramatically fewer basis functions than a comparable SVM. These include the benefits of probabilistic predictions, automatic estimation of `nuisance' parameters and the facility to utilize arbitrary basis functions. The experiment is carried out with the help of MATLAB and WEKA by using KDD Cup 1999 dataset and the results indicate that the proposed technique can achieve higher detection rate and very low false alarm rate than the regular SVM algorithms.