Integrating Intrusion Detection Model Using Ensemble Of Classifiers And Extreme Learning Machine

The Intrusion detection from the last some decades are very important for current as well as for present networks. In the recent times many new methods have been used for IDS with machine learning technique and analysis for huge data is very much suitable. But the techniques like WMV (weighted majority voting) which have large dataset will take much more amount of time with this there is degradation of results whenever increasing the dataset. For this problem this paper focuses on the Extreme learning machine would be the best suitable for IDS with the analysis of big data and improving the accuracy. The proposed technique will integrate Mutual information ranking filter and attribute ranking feature selection with ELM technique. The Mutual information technique will implement attribute selection and will analyse proposed technique performance MI-ELM technique with algorithms like Modified Naïve Bayes, Support vector machine, LP Boosting and also hybrid of these three algorithms with respect to Precision, Recall. F-measure, accuracy, the (KS) Kappa statistic, Incorrectly and Correctly (CI) classified instances, RMSE (Root of Mean square erratum or error) and RRE (Root relative of error). There will be analysis of the dataset on according to basis of traffic is it normal or abnormal and the experimental results has shown that there will be increased accuracy in comparison with the classifiers.