Platform Utilizing Similar Users’ Data to Detect Anomalous Operation of Home IoT Without Sharing Private Information

To mitigate the risk of cyberattacks on home IoT devices, we have proposed a method for detecting anomalous operations by learning the behaviors of users based on the operation sequences of their home IoT devices and home conditions. While this method requires a sufficient amount of training data, achieving accurate detection is still possible by utilizing the data of users with similar lifestyles. However, users are unwilling to share their private information with others. In this study, we propose a platform to utilize data of similar users without sharing private information. We introduce an agent that learns behaviors of users to detect anomalous operations in each home and cooperates with other agents. In this framework, an agent requiring cooperation with other agents sends a question to the other agents, attaching identifiers of past questions that are similar to the behaviors learned. The receivers decide whether the question is from a similar agent by using the attached information. If the question is from a similar agent, the agent answers the question. We evaluate our platform by using behavior datasets collected from real homes. We simulate two cases: (1) sequences of operations are monitored, and (2) home IoT devices are used alone but sequences cannot be used for detection. The results show that our framework has a 50.5% higher detection ratio for case (1) when using the behavioral data of each user. For case (2), our framework has a 13.4% higher detection ratio when using all the behavioral data of users.