AN INTELLIGENT SYSTEM FOR DETECTING ANOMALIES AND IDENTIFYING SMART HOME DEVICES BASED ON THE COLLECTIVE COMMUNICATION

Keywords: smart home, network traffic, behavior profiling, communication, abnormal behavior, classification.

Abstract

The fourth industrial revolution put new processes on the rails 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. At the same time, these processes are not bypassed by home automation systems or smart homes. A smart home is defined as a system of interconnected sensors, actuators, and other devices that are networked together with computer systems and controlled by appropriate software. This connection allows to collect, share and analyze data, which helps to increase comfort, automation and control over the parameters of the house. However, along with the obvious advantages and conveniences of rooting home automation systems, this concept leaves a number of potential security bottlenecks for attackers. Data collected by smart devices is always point of interest to hackers and hijackers of confidential information.
Third-party access to data collected by smart devices can lead to a variety of emergencies, the degree of danger of which will depend solely on the wish of the owner of the intercepted data. The paper proposes an intelligent 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. Profiles consist of a set of features that describe the behavior of smart devices on the network, including the period of activity of the device and the period of its sleep. 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.

Author Biographies

Андрій Олександрович Нічепорук, Khmelnytskyi National University

 PhD, Docent, Associate Professor at the Department of computer engineering and system programming,

Анастасія Андріївна Нічепорук, Khmelnytskyi National University

 PhD Student at the Department of computer engineering and system programming

Олег Станіславович Савенко, Khmelnytskyi National University

 Dr. of Science, Professor, Dean of the Faculty of Programming and Computer and Telecommunication Systems

Андрій Дмитрович Казанцев, Khmelnytskyi National University

PhD Student at the Department of computer engineering and system programming

Published
2021-09-02
Section
Information Systems and Technologies