Network Attack Traffic Recognition Based on Quantum Neural Network
Meng Zhang, Bo Lv, Zi-shan Liu · 2022
Network attacks are increasingly being paid attention to. People are constantly studying effective attack recognition and prevention methods. This paper presents a method of network attack traffic recognition based on quantum neural network for the first time. The problem of gradient explosion is avoided by making full use of the bounded gradient function of quantum neural network. A method of transforming the classical traffic characteristic value data into quantum state representation is proposed. And the preliminary experimental verification is carried out on Google Tensorflow Quantum platform. The feasibility of the method is verified.