Smart Appliance Abnormal Electrical Power Consumption Detection

Abstract

Potential cyber threats now have an immensely larger attack surface due to the widespread use of smart devices and smart environments. Smart home appliances build a network of linked objects that exchange information and communicate with each other. Detecting abnormal electrical power consumption becomes a first line of protection for bolstering the security of smart homes. Using Machine Learning (ML), anomalous electrical power consumption of the Smart Appliance can be identified. This work proposes an ML-based anomalous electrical power consumption detection to identify the security breach of the Smart Appliances. SimDataset is used for anomalous power consumption detection as a proof of concept for experimentation, and results depicted that Random Forest (RF) classifier outperformed other ML-based classifiers while detecting the abnormal electrical power usage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.