Stochastic Machine Learning Based Attacks Detection System in Wireless Sensor Networks

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.