Cooperative attacks detection based on artificial intelligence system for 5G networks

Abstract

The Fifth Generation (5G) network is vulnerable to a couple of attacks targeting the main segments of 5G architecture, user equipment, radio communication, edge and core network. Therefore, a reliable and accurate security mechanism is mandatory to protect the end-to-end network against the internal and external attacks. In this research work, I propose a hierarchical detection scheme based on a reinforcement learning process to secure the main segments of end-to-end 5G network. Specifically, a distributed attacks detection systems collaborate with a goal to reinforce their learnings, update their optimal defense strategies, and determine the current and futures attacks ‘misbehaviors occurred at different segments. Experimental results demonstrate that, the proposed cyber defense scheme requires a low computation overhead to protect the network from internal and external attacks as compared to the current cyber detection schemes.