Supervised Machine Learning Classification Algorithmic Approach for Finding Anomaly Type of Intrusion Detection in Wirel
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upervised Machine Learning Classification Algorithmic Approach for Finding Anomaly Type of Intrusion Detection in Wireless Sensor Network Ashwini B. Abhalea, * and S. S. Manivannanb a
D.Y. Patil College of Engineering and Technology, Akurdi, India b Vellore Institute of Technology, Vellore, Tamil Nadu, India *e-mail: [email protected]
Received November 21, 2019; revised March 11, 2020; accepted May 25, 2020
Abstract—From the last decade, the use of internet and its growth is continuously increasing. Similarly, numbers of services are coming out along with the internet and it is being used for providing facilities to human beings. Wireless sensor have been used for various application such as fire safety, military application, petroleum industry, security system, monitoring and environmental condition and many more. WSN node exposes itself to various security related attacks due to low battery power supply, low bandwidth support, data transmission over multi hop node, dependency on intermediate or other nodes, distributed in nature and self-organization. The WSN attacks observe in all layers of OSI model. Wireless sensor nodes has various issues because of that, it experiences number problem related to its functionalities and some malfunction due to attacks. It is require to build defence and network monitoring system for identifying attacks and prevent them. Intrusion detection system (IDS) plays an important role to detect threads inside the system and generate the alert related to the attack. In this work, supervised classification models for intrusion detection are built using such as Random Forest classifier, Support Vector Machine, Decision Tree Classifier, LGBM Classifier, Extra Tree Classifier, Gradient Boosting Classifier, Ada Boost Classifier, K Nearest Neighbour Classifier, MLP Classifier, Gaussian Naive Bayes Classifier and Logistic Regression Classifier. The NSLKDD, i.e. Modified version of the KDD99 Data Set on which we checks these algorithms. Experimental results how the highest accuracy relative to other classification systems in the support vector machine. Keywords: machine learning algorithm, classification, wireless sensor network, intrusion detection system, accuracy, performance matrix DOI: 10.3103/S1060992X20030029
1. INTRODUCTION Wireless Sensor Network (WSNs) is formed by the combination of Sinks and Sensor Nodes. The sensor node is main unit in WSNs and sensor network has the ability to self-organize. Self-Organization formed by sensor node is completely distributed and decentralized in nature. Wireless Sensor Network (WSNs) is formed by the combination of Sinks and Sensor Nodes. The sensor node is main unit in WSNs and sensor network has the ability to self-organize. Self-Organization formed by sensor node is completely distributed and decentralized in nature. The Wireless Sensor network is formed using centralized or distributed techniques [1]. The communication is made between the nodes via intermediate multi-hop nodes. The responsibility of sensor node is to gather information f
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