A Hybrid Approach for Feature Selection Analysis on The Intrusion Detection System Using Navi Bayes And Improved BAT Algorithm

Abstract

In recent days, millions of people in many institutions communicate with each other on the Internet. The past two decades have witnessed unprecedented levels of Internet use by people around the world. Almost alongside these rapid developments in the internet space, an ever-increasing incidence of attacks carried out on the internet has been consistently reported every minute. In such a difficult environment, Anomaly Detection Systems (ADS) play an important role in monitoring and analyzing daily internet activities for security breaches and threats. However, the analytical data routinely generated from computer networks are usually of enormous size and of little use. This creates a major challenge for ADSs, who must examine all the functionality of a certain dataset to identify intrusive patterns. Article collection remains an imperative factor in the modeling of anomaly-based intrusion detection system. Irrelevant characteristics may lead to over fitting, which in turn affects the modeling ability of the classification algorithm. The purpose of this research is to analyze and select the most distinguishing input features to construct an efficient and computationally efficient ADS solution. In the first step, based on the concept of entropy, by selecting the optimal subset, a heuristic algorithm NAIBA is proposed for dimensionality reduction. Then, the relevant and meaningful features are selected, before implementing Number of Classifiers which includes: (1) An irrelevant feature can lead to over fitting which in turn negatively affects the modeling power of the classification algorithms. Experiment was done on CICIDS-2017 dataset by applying (1) Random Forest (RF), (2) Bayes Network (BN), (3) J48 and (4) Random Tree (RT) with results showing better detection precision and faster execution time. The proposed heuristic algorithm outperforms the existing ones as it is more accurate in detection as well as faster. However, Random Forest algorithm emerges as the best classifier for feature selection technique and scores over others by virtue of its accuracy in optimal selection of features