Improvement of Wireless Sensor Networks Against Service Attacks Based on Machine Learning

Improvement of Wireless Sensor Networks Against Service Attacks Based on Machine Learning
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© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Gang Xu, Allemar Jhone P. Delima, Ivy Kim D. Machica, Jan Carlo T. Arroyo, Zhengfang He, Weibin Su
DOI :  10.14445/22315381/IJETT-V70I5P209

How to Cite?

Gang Xu, Allemar Jhone P. Delima, Ivy Kim D. Machica, Jan Carlo T. Arroyo, Zhengfang He, Weibin Su, "Improvement of Wireless Sensor Networks Against Service Attacks Based on Machine Learning," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 74-79, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P209

Abstract
With the rapid development of sensor technology, wireless sensor networks (WSNs) composed of a large number of low-cost, high-performance and plug and play sensor nodes have occupied more and more application scenarios in society, such as medical and health, environmental monitoring, business activities, and national defense security. However, a WSN is a distributed network exposed in an open environment. Each node is independent of the other, and the lack of a central node and monitoring node makes it vulnerable to malicious attacks, and it is difficult to prevent. Denial of Service (DoS) attack is one of them. Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms prevent network attacks, but these methods have specific disadvantages such as low quality, complex time, and data loss. This paper proposes a method using Random Support Vector Regression (RSVC) to improve the quality of preventing distributed denial of service (DDoS) attacks in WSN. It provides the results of various simulation scenarios and compares the corresponding data. The research on DDoS prevention is very helpful in understanding the anti-attack performance of wireless sensor network nodes. The impact of DoS attacks on wireless sensor networks` performance is considered the key research of these problems.

Keywords
Machine learning, random support vector regression (RSVR), support vector machine (SVM), wireless sensor network (WSN).

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