Abstract
In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifiers are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.
Highlights
In modern world internet growth and user is rapidly increase with a high benefit for the development of e-government and e- commerce
True Negative Rate = TN/(FP+TN) Experimental result I NSL KDD dataset used for testing the performance of the machine-learning model that is K-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM)
Experimental result V The fifth experiment was performed with a comparison of three Classifier that is KNN, NB and SVM using 20% and 10% NSL KDD dataset with 41 features and 27 features
Summary
In modern world internet growth and user is rapidly increase with a high benefit for the development of e-government and e- commerce. Protect network environment is maintained by Intrusion detection system (IDS). Intrusion detection system is hardware or software that observes the data traffic carefully identify the Manuscript received on April 01, 2021. Time many organization practice intrusion detection systems to protect their system from intrusion. This technology is suffering a common problem, which is generating huge number of false alarm and contains high number of data features. To select the best classifier by studying a comparison analysis of this machine learning use NSL KDD standard dataset and using the combination of Chi square and Extra Tree feature selection method. For simulation of the NSL KDD dataset, we use python programing
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