Abstract
The utilization of information from the radio channel is useful in detecting the spoofing attacksin 5G wireless communications. This concept has been used for a wide range of uses for the Internet of Things (IoT) environment by users and their IoT devices. But how these tasks can minimise the effects of cyber threats in genuinely complex networks has not yet been sufficiently addressed. These are seriously exposed, as is the case with 5G broadband networks due to a wide variety of technology at various abstract stages. As 5G IoT is the 5G environment, including the IoT background, in this article, we are an artificial intelligence (AI) intended to minimise the impact of 5G IoT threats, when extended to the participants involved on a number of levels.This paper uses Support Vector Machine (SVM) based PHY-layer authentication algorithm to detect the possible security attacks in 5G wireless communication at physical layer. It is utilized in increasing the rate of authentication with test features. The detection rate is improved further with test statistic features. The model is implemented on multiple-input multiple-output (MIMO) channel. The simulation results shows that the proposed method yield the high detection rate on all attacks.
1. Introduction
The fifth generation (5G) delivers high bandwidth, high performance and security standards in 3 common scenarios: enhanced mobile connectivity, wide-scale stuff web and low-latency connections [1 2]. The special situation for mobile broadband and highfrequency, is used in indoor or urban environments in which wide-ranging mobile network signals are continuously secured in rural areas. Meantime, the 5G calls for a large range of devices and installations to be integrated, and this is an integral necessity for IoT service[3]. Manual portable devices join a cellular network, which ensures that wireless authentication is also heavily employed. Therefore, lightweight networking approaches for intensive deployment scenarios of 5G networks.
