Anomaly Detection in Smart Home Networks Using Kalman Filter

Throughout the recent decades, smart homes have made an enormous expansion around the world among residential customers; hence the most intimate place for people became connected to cyberspace. This environment attracts more hackers because of the amount and nature of data. Therefore, the cybersecurity in smart home is a real concern for many reasons, and the conventional security methods are not effective in the smart home environment. Therefore, there is an insistent demand to detect these malicious attacks that target smart appliances in smart homes. This paper proposes a novel approach as a real-time cybersecurity monitoring system based on tracking smart home behavior. We utilize a Kalman Filtering algorithm to create an optimal normal behavior for the smart connected devices. Furthermore, energy consumption is utilized as an input to the proposed system, and the Shapiro-Wilk test to detect abnormal behavior. This composite approach leverages the well-known power of Kalman filter and Shapiro-Wilk test to guarantee the effectiveness of the approach. Moreover, energy consumption is an optimal way that exposes smart devices' behavior which makes the other two components more powerful.