Anomaly Detection in Edge Devices
Résumé
With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This applies also to resourceconstrained IoT and edge devices, which can benefit of relatively small-but smart-local anomaly detection tasks to detect failures and attacks. This paper overviews the process we are following to equip small devices with anomaly detection capabilities to make them self-aware of their health state and take appropriate countermeasures. Our process is applied to a Linux-based devices named ARANCINO, which has already been successfully used in several smart cities applications.
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