Machine Learning-Based Attack Detection for Wireless Sensor Network Security Using Hidden Markov Models

• Published in: Wireless personal communications 2024, Vol.135 (4), p.1965-1992
• Main Authors: Affane M., Anselme R., Satori, Hassan, Boutazart, Youssef, Ezzine, Abderahim, Satori, Khalid
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Classification; Communications Engineering; Computer Communication Networks; Datasets; Engineering; Machine learning; Markov chains; Network security; Networks; Probabilistic models; Sensors; Signal,Image and Speech Processing; Stochastic models; Wireless sensor networks
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-024-10999-3

The progress of Wireless Sensor Networks (WSNs) technologies has introduced a greater susceptibility of sensors and networks to being victims of distributed attacks. These attacks include various malicious activities such as intrusions during routing processes, data intercepting and other disruptive actions. In response to this increasing security challenge, numerous models for attack identification have been proposed. These models typically involve the deployment of detection systems that collect sensor data and employ machine learning and artificial intelligence techniques to categorize them. This research introduces a novel method for the analysis and classification of WSN datasets. The primary objective is to develop an anomaly identification approach that enhances sensor network security and operational efficiency with a good degree of accuracy. To achieve this goal, artificial intelligence method based-on stochastic models are used to create a detection system that learns from existing routing data to identify potential malicious network entries. The proposed approach relies on the principles of the Hidden Markov Model (HMM) and the Gaussian Mixture Model (GMM), a part of artificial intelligence stochastic functions, which incorporate predictive assumptions. In addition, dimensionality reduction is used to select the most pertinent routing features for the training of the system. To assess the effectiveness of our proposed approach, we performed experiments using a custom dataset that represents various network scenarios, including both normal and attacked states. The results demonstrate the performance of the model, achieving a classification score of 92. 18% when using a combination of two HMM and three GMM in the classifier. The proposed method attains a 98% precision value and 95% accuracy, better than performances of SVM, NB, DT and RF methods. This highlights the efficacy of our proposed approach compared to existing research.