Review of Deep Learning Methods for Multi‐Channel Intelligent Attack Detection: Exploring Deep Learning Methods for Multi-Channel Intelligent Attack Detection in Wireless Communication Systems

Abstract

New challenges have emerged in wireless communication system since the fifth the fifth-generation networks and artificial intelligence technologies have been developed, especially when it comes to cybersecurity. In this review paper, we have a look on methods of detection for attacks that uses techniques and strength of deep learning. In particular, we first and foremost sum up central issues of organization security and attack recognition and present a few related applications utilizing profound learning structure. “Based on classification on profound learning techniques, we give unique consideration to attack identification strategies based on various types of structures, like autoencoders, neural networks, and convolutional neural networks. Subsequently, we present some benchmark datasets with depictions and contrast the exhibition of addressing approaches with show the current working condition of attack recognition techniques with profound learning structures. At long last, we sum up this paper and talk about certain ways of working on the exhibition of attack discovery under contemplations of using deep learning structures.”