An outlier-based analysis for behaviour and anomaly identification on IoT sensors Online publication date: Wed, 29-Jun-2022
by Felipe C. Almeida; Adilson E. Guelfi; Anderson A.A. Silva; Norisvaldo Ferraz Junior; Marvin O. Schneider; Vagner L. Gava; Sergio T. Kofuji
International Journal of Sensor Networks (IJSNET), Vol. 39, No. 2, 2022
Abstract: The pervasive nature of WSN-based IoT devices provides benefits for the industry, healthcare, and other environments. Because of that, a secure network that identifies sensor measurement errors is essential. However, the sensors are sensitive to changes according to the environment in which they are installed. Therefore, this work proposes an outlier-based analysis for behaviour and anomaly identification on IoT sensors which is twofold: first, cluster the sensors based on the variance of the devices' sensors; next, identify anomalies applying Mahalanobis distance within the clusters to identify anomalous devices. We use different datasets in the experimental process to validate our proposal. As a result, we demonstrate anomalous sensors identification without relying only on the neighbour measurements (which could lead to incorrect identification of anomalies), and by segregating the sensors' behaviour based on daytime.
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