Decision-Tree Based Root Cause Localization for Anomalies in Smart IoT Systems

Abstract

With the rapid growth of Internet of Things (IoTs), Internet-connected devices and home appliances gain popularity on the consumer electronic market. New home IoT products with built-in network connections and intelligent functionalities are quickly rolled out to the market. As predicted by Gartner, there will be more than 500 IoT devices deployed in a typical household by 2022. The easy device integration and advanced automation logic also brings new challenges with regard to security and privacy. IoT devices have been reported as unreliable because of the constraints in costs and resources. Anomalies of IoT devices include malfunctions of the physical part or the cyber part of an IoT device, as well as abnormal behaviors due to malicious attacks. Abnormal IoT devices could cause severe consequences, because they reside in the home environment and have critical functions that can change the physical world, such as door (smart lock) opening, smart oven burning (which could cause fire), or smart water valve opening (which could cause flooding). In this paper, we study the important issue of localizing the root cause of anomalies in a smart environment (e.g., smart homes and smart offices). We propose to use decision trees for efficient and effective anomaly root cause localization. We construct decision trees from automation rules that control the operations of smart IoT devices in a smart environment. Our performance evaluation on data collected from real smart homes demonstrate the effectiveness of our proposed approach.