Stochastic Machine Learning Based Attacks Detection System in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a crucial role in diverse applications, encompassing environmental monitoring, healthcare, and industrial automation. However, these networks are susceptible to various security threats, underscoring the need for robust attack detection systems. In this paper, we...

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Vydané v:	Journal of network and systems management Ročník 32; číslo 1; s. 17
Hlavní autori:	Moundounga, Anselme Russel Affane, Satori, Hassan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.03.2024 Springer Nature B.V
Predmet:	Accuracy Amalgamation Automation Communications Engineering Comparative analysis Computer Communication Networks Computer Science Computer Systems Organization and Communication Networks Efficacy Environmental monitoring Experiments Grammatical aspect Health care Health services Industrial automation Information Systems and Communication Service Iterative methods Machine learning Markov analysis Markov chains Networks Operations Research/Decision Theory Principal components analysis Probabilistic models Security Wireless communication systems Wireless sensor networks Dimensionality reduction Secure routing algorithm Hidden Markov models Machine learning Gaussian mixture models Stochastic attacks detection system Wireless sensor networks (WSNs)
ISSN:	1064-7570, 1573-7705
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Abstract	Wireless Sensor Networks (WSNs) play a crucial role in diverse applications, encompassing environmental monitoring, healthcare, and industrial automation. However, these networks are susceptible to various security threats, underscoring the need for robust attack detection systems. In this paper, we propose a Stochastic Machine Learning-Based Attack Detection System for WSNs that leverages the synergy of Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs). The proposed system employs Principal Component Analysis for dimensionality reduction in the WSN dataset, thereby retaining essential routing features while mitigating the number of variables. Additionally, iterative machine learning Expectation-Maximization is employed to train the HMMs and GMMs, empowering the system to accurately detect and classify malicious activities and erroneous routing data. To evaluate the system’s efficacy, a series of experiments were conducted, entailing variations in the parameters of both HMMs and GMMs. Notably, the findings underscore that the configuration comprising 3 HMMs and 4 GMMs surpasses other combinations, achieving an exceptional accuracy level of 94.55%. Furthermore, a comprehensive comparison is drawn between the proposed system and common machine learning classifiers. This analysis unequivocally highlights the system’s superiority in terms of accuracy and overall performance. Notable is the system’s exceptional performance in cross-validation, consistently achieving accuracies within the range of 0.96 to 0.98. The proposed Stochastic Machine Learning-Based Attack Detection System introduces a highly promising approach to fortify the security of WSNs. The amalgamation of rigorous experimentation, comparative analysis, and impressive results underscores its potential as an effective security enhancement tool.
AbstractList	Wireless Sensor Networks (WSNs) play a crucial role in diverse applications, encompassing environmental monitoring, healthcare, and industrial automation. However, these networks are susceptible to various security threats, underscoring the need for robust attack detection systems. In this paper, we propose a Stochastic Machine Learning-Based Attack Detection System for WSNs that leverages the synergy of Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs). The proposed system employs Principal Component Analysis for dimensionality reduction in the WSN dataset, thereby retaining essential routing features while mitigating the number of variables. Additionally, iterative machine learning Expectation-Maximization is employed to train the HMMs and GMMs, empowering the system to accurately detect and classify malicious activities and erroneous routing data. To evaluate the system’s efficacy, a series of experiments were conducted, entailing variations in the parameters of both HMMs and GMMs. Notably, the findings underscore that the configuration comprising 3 HMMs and 4 GMMs surpasses other combinations, achieving an exceptional accuracy level of 94.55%. Furthermore, a comprehensive comparison is drawn between the proposed system and common machine learning classifiers. This analysis unequivocally highlights the system’s superiority in terms of accuracy and overall performance. Notable is the system’s exceptional performance in cross-validation, consistently achieving accuracies within the range of 0.96 to 0.98. The proposed Stochastic Machine Learning-Based Attack Detection System introduces a highly promising approach to fortify the security of WSNs. The amalgamation of rigorous experimentation, comparative analysis, and impressive results underscores its potential as an effective security enhancement tool.
ArticleNumber	17
Author	Moundounga, Anselme Russel Affane Satori, Hassan
Author_xml	– sequence: 1 givenname: Anselme Russel Affane surname: Moundounga fullname: Moundounga, Anselme Russel Affane organization: Department of Computer Science, Faculty of Sciences Dhar-Mahraz, Sidi Mohamed Ben Abdallah University – sequence: 2 givenname: Hassan surname: Satori fullname: Satori, Hassan email: hassan.satori@usmba.ac.ma organization: Department of Computer Science, Faculty of Sciences Dhar-Mahraz, Sidi Mohamed Ben Abdallah University
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Keywords	Dimensionality reduction Secure routing algorithm Hidden Markov models Machine learning Gaussian mixture models Stochastic attacks detection system Wireless sensor networks (WSNs)
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