Optimized Performance of Attacks Detection in WSN based on Machine Learning Algorithms

Farid, A. A., Khalil, A. T., Shawky, M. A. , Samrah, A. S. and Ibrahim, H. (2023) Optimized Performance of Attacks Detection in WSN based on Machine Learning Algorithms. In: International Telecommunications Conference (ITC-Egypt'2023), Alexandria, Egypt, 18-20 July 2023, ISBN 9798350326062 (doi: 10.1109/ITC-Egypt58155.2023.10206348)

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Abstract

Wireless sensor networks (WSN) are a type of wireless network composed of numerous sensors that collaborate to sense, collect, process, and transmit information about the physical environment within the network’s geographical area. The information is ultimately received by the network owner. However, typical attacks such as Blackhole, Grayhole, Flooding, and Scheduling can pose a significant threat to the WSN, potentially causing significant damage to the system in a short period. Detection methods, such as snooping, have demonstrated low detection and high false alarm rates, and require significant computational resources. Additionally, they tend to produce redundant network data. To address these limitations, we propose a novel intervention approach called “Ensemble Bagged Trees,” which employs a squared backward sequence selection (SBS) algorithm to reduce data dimensionality and computational overhead in the feature space of native traffic data. The Ensemble Bagged Trees algorithm is then utilized to detect various network attacks. Experimental results using the WSN-DS dataset demonstrate that the proposed method outperforms typical machine learning detection algorithms, with a detection rate of 99.1% for the normal black hole, gray hole, flood, and tabulation attacks.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Shawky, Mr Mahmoud
Authors: Farid, A. A., Khalil, A. T., Shawky, M. A., Samrah, A. S., and Ibrahim, H.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
ISBN:9798350326062
Copyright Holders:Copyright © 2023 IEEE
First Published:First published in 2023 International Telecommunications Conference (ITC-Egypt)
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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