Security and Privacy Protection of Internet of Things Devices in 6G Networks

With the gradual maturity of 6G (sixth-generation mobile communication network) technology, the security and privacy protection of Internet of Things (IoT) devices have become the research focus areas. This paper discusses how to improve the security and privacy protection level of IoT devices in a 6G network environment to deal with the low success rate of device authentication, inaccurate detection of malicious behavior, poor vulnerability detection and repair, and weak anti-attack capabilities. In this study, a security architecture for IoT smart devices for 6G networks is first designed; then, a full-dimensional dynamic convolutional network (FDCN) is introduced to learn the IQ (in-phase and quadrature) signal characteristics of the device and complete the device authentication; then, malicious programs are detected through feature differences to achieve security monitoring; finally, a set of vulnerability repair processes is designed. Through experimental verification, the method proposed in this paper has a device authentication success rate of 92% to 100% when the number of authentication attempts is 10 to 50; when the sample size is 100 to 5000, the malicious behavior detection accuracy rate remains between 88 and 95%; in addition, the vulnerability detection time and anti-attack ability have also been significantly improved. Compared with existing methods such as blockchain, edge computing, and joint learning, this method reduces the vulnerability detection time by 12.62 s, 15.08 s, and 13.43 s, respectively; when the attack complexity is 10, the attack resistance reaches 93.59%, which is much higher than other methods.