Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers 
Surafel Mehari Atnafu1, Anuja Kumar Acharya2
1Surafel Mehari Atnafu, M.Tech degree in Computer Science and Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India.
2Prof (Dr.) Anuja Kumar Acharya, PhD, Computer Science, Bhubaneswar, India.
Manuscript received on 01 April 2021 | Revised Manuscript received on 07 April 2021 | Manuscript Accepted on 15 April 2021 | Manuscript published on 30 April 2021 | PP:22-28 | Volume-1 Issue-2, April 2021 | Retrieval Number: 100.1/ijainn.B1025021221 | DOI: 10.54105/ijainn.B1025.041221
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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.
Keywords: Classifiers, False Detection, Python, NSL KDD, Intrusion Detection, Machine-learning
Scope of the Article: Data Mining and Machine Learning Tools