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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Veröffentlicht in:Journal of network and systems management Jg. 32; H. 1; S. 17
Hauptverfasser: Moundounga, Anselme Russel Affane, Satori, Hassan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2024
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1064-7570
1573-7705
DOI:10.1007/s10922-023-09794-5