Management methods in improving the security of ۵G networks

Fifth generation (۵G) mobile networks are recognized as an essential communications technology for today's society. The growing proliferation of security threats in these networks has highlighted the need for new and robust solutions in the security sector. ۵G and beyond networks are expected to deliver: very high data transfer speeds, better quality of service, improved network security, high capacity, low latency and low costs. In this study, we research and implement security improvements on ۵G networks using the Q-Learning algorithm. These algorithms have the ability to learn and improve over time, and can adapt automatically and flexibly to new security threats. In the proposed method, a learning model based on Q-Learning is added to each node. The task of this model is to determine the validity of neighboring nodes. Therefore, the validity of each node is evaluated by other neighboring nodes. First, security threats related to ۵G networks will be identified and then appropriate learning algorithms will be used to improve security. The results show that using deep learning algorithms can automatically identify and eliminate security issues, while implicitly providing the ability to determine the best route to send data. In addition to security, these results in increased speed and accuracy of data transmission. The proposed method can increase the average successful parcel delivery rate to ۸.۱۳%, while reducing the end-to-end latency to ۱۹.۲۶%, it can also improve the performance of the systems Intrusion detection with an average accuracy of ۹۸.۹%. Simultaneously; It also works better to more accurately identify each type of attack.

کلیدواژه ها:

fifth generation network ، routing ، Internet of Things ، reinforcement learning