Anomaly based detection in time series data on IoT systems using statistical models

Abstract

The internet of things (IoT) has become a real revolution that represents technological innovation in all domains, it becomes more and more integrated into human activities starting from personal needs, to professional eras including industry, logistics, healthcare. Yet this technology didn’t only bring advantages to all industries, it also creates new challenges mostly security-related, due to the specifications of the IoT environment: its heterogeneity, the restricted resources, and the continuous enormous sensitive data generated and exchanged on the IoT ecosystem. In this paper, we propose a study of statistical models (autoregression, moving-average, autoregression-movingaverage, autoregression integrated movingaverage, seasonal autoregression integrated movingaverage) to build an anomaly-based detection model for times series data, to detect abnormal behavior that can be explained by a sensor failure, or a compromised sensor. The proposed anomaly-based detection relies on defining the data behavior and creating a profile on the assumed normal state, the created profile will be used as knowledge to predict future values to which real records will be compared, any deviation from the predicted data will be considered as abnormal, that indicates an anomaly has occurred on the sensor or the exchanged data. The proposed approach will help improve accuracy, reliability, trustworthiness, and data integrity.