An implementation of an artifact for security in 5G networks using deep learning methods

Abstract

The Fifth-Generation networks are vital in telecommunications, which reaches a substantial increase in transmission speed. However, due to this dynamism, cyber attackers try to take advantage of certain configuration flaws. Under this problem, this study aims to design and implement an artifact capable of protecting and improving security in this cellular network type. The artifact uses a neural network structure based on Depp Learning, Gödel's theorem, Dijkstra's graph theory, a sigmoid function for faster activation in the designed neural network, and a mathematical model able to perform a recursive and logical process for the analysis of attacks. Considering the Gödel method, this artifact can evade Worms, Ransomware, Phishing, Doxing attacks and can be used in the OPC, Profibus, EtherCat, Profinet DNP3, and Modbus protocols. Its implementation allows us to create evasive actions in case of an attack and improve the configuring flaws in the security protocols, changing its parameters and making it secure. We developed the artifact through Extreme Programing with the combination of Python and Matlab. The results prove the functionality of the algorithms and demonstrate the success in evading an attack or making the best decision to protect the 5G network.