A System for Detecting Anomalies and Identifying Smart Home Devices Using Collective Communication.

The fourth industrial revolution put on new rails processes of automation in industry, healthcare, home and other areas of human life through the mass integration of the concept of the Internet of Things into these areas. However, this concept leaves a number of potential "bottlenecks" in the security of such systems for attackers. Third-party access to data collected by smart devices in, for example, a smart home can lead to a variety of emergencies, the degree of danger of which will depend solely on the will of the owner of the intercepted data. In this paper we proposes a system for detecting anomalies and identifying smart home devices based on the collective communication of smart homes. The concept of the system is based on the benefits of combining smart homes into a social network in terms of improving the security of both a single smart home and the entire social network of combined smart homes. Detection of anomalies and identification of devices in each of the smart homes is based on monitoring network traffic and forming profiles of smart devices that are present in the network. Based on this, a whitelist of allowed profiles of devices operation in the cluster is formed. To verify the presence of a profile in the whitelist the Random Forest algorithm was used. A key feature of the system is the communication of smart home clusters with each other to exchange information about the available smart device profiles in the whitelists of each cluster. To evaluate the effectiveness of the proposed system, a number of experimental studies were conducted. The results of the experiments showed the overall accuracy of the system at the level of 97.21% with an average level of type I errors of 5.94%.