Malicious attack detection based on continuous Hidden Markov Models in Wireless sensor networks

Alongside the rapid progress of Wireless sensor networks (WSNs) technologies, sensors and networks can rapidly be a victim of distributed attacks. Attackers can perform intrusions to break down the network during the routing process and intercept gathered data by dropping them for example. To prevent the increase of security issues in WSN, many attack identification methods and models were proposed in which detection systems are deployed to collect sensed data and categorize them using machine learning and stochastic binary-classification techniques. For this work, a new method is introduced to analyze and classify an own WSN dataset. We aim to design an anomaly identification approach to improve the sensor network security and its efficiency with high accuracy. To reach this goal, machine learning approaches are used to define a detection system that learns from routing datasets to identify network malicious entries. The proposed models are based on Hidden Markov Model (HMM), and Gaussian Mixture Model (GMM) stochastic assumptions. Also, the dimensionality reduction technique was deployed to select the most relevant features for the training. The experimentation phase was realized on its own and made the dataset that reflects different network situations of normal and attacked cases. The outcomes performances of the proposed method were obtained with a classification accuracy of 92.18% using a 2 HMM/3 GMM classifier. This result demonstrates the quality of our proposed approach compared with existing literature and its usefulness to improve the security of WSN.