Intrusion Detection System for 5G Device-to-Device Communication Technology in Internet of Things

Abstract - The emergence of Internet of Things (IoT) has raised the need for high quality communications, and high performance networks. 5G cellular communication network exhibits the readiness to provide such high quality communication channels by using various advanced technologies. Device to device communications is a one of multiple technologies that have been suggested in 5G. By the employment of this technology, mobile devices can communicate with each other without the involvement of a base station (BS) which eliminates congestion, expands coverage area and increases throughput. Communicating devices set up a multi-hop path using nearby devices which act as relaying elements, or routers. However, the Self-organizing nature and the lack of centralized control of D2D make it easier to launch multiple types of attacks. In this paper, an intrusion detection system IDS is proposed using machine learning techniques. Eight types of attacks are considered to train the system for intrusion detection, then, multiple classification algorithms have been compared. Finally, a multiobjective model has been designed based on the results of comparison to secure the communication process under D2D technology. The used dataset is generated using Network Simulator NS-2.