A random forest algorithm under the ensemble approach for feature selection and classification

Over the years, research analysts have proposed diverse intrusion detection systems' (IDS) tactics to manage the increasing number and complexity of computer threats. IDS takes all the data over the network and analyses the data using machine learning for finding the attacks. It is tough to find attacks on the network because it contains fewer records than standard data. It is significantly challenging to design an IDS for high accuracy. It also foregrounds different feature selection methods to select the best feature subset. We use the random forest feature importance for finding the best features. Single classifiers can mislead the find result, so we use random forest as classification with the help of best features. The proposed model is assessed on standard datasets like KDD'99, NSL-KDD, and UNSW-NB15. The experimental result shows that the proposed model outperforms the existing methods in terms of accuracy, detection rate, and false alarm rate.