Abstract
With the development of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have emerged to wireless communication system, especially in cybersecurity. In this paper, we offer a review on attack detection methods involving strength of deep learning techniques. Specifically, we firstly summarize fundamental problems of network security and attack detection and introduce several successful related applications using deep learning structure. On the basis of categorization on deep learning methods, we pay special attention to attack detection methods built on different kinds of architectures, such as autoencoders, generative adversarial network, recurrent neural network, and convolutional neural network. Afterwards, we present some benchmark datasets with descriptions and compare the performance of representing approaches to show the current working state of attack detection methods with deep learning structures. Finally, we summarize this paper and discuss some ways to improve the performance of attack detection under thoughts of utilizing deep learning structures.
Highlights
Security and Communication Networks is the foundation of our paper
Different from a complete view on this specific domain brought by Berman et al [5], Apruzzese et al [4] focus on explaining attack detection methods related to intrusion detection, malware analysis, and spam detection
Unlike former methods, we intend to build our paper on the basis of deep learning models, paying special attention to attack detection methods built on different kinds of deep learning architectures
Summary
Security and Communication Networks is the foundation of our paper. For example, Berman et al [5] provide a quantity of reading resources to describe the basic knowledge and development history of deep learning methods and their corresponding applications in attack detection. Afterwards, Aleesa et al [3] review and analyze the research status of intrusion detection system based on deep learning technology among four major databases. They offer a systematic literature review of the relevant articles using the keywords “deep learning”, “invasion”, and “attack” selection, which provide a wide range of resource background for the researchers. We attempt to build up basics for future research through a thorough literature review of deep learning related approaches in the field of attack detection. Afterwards, we make a brief representation of successful applications for cybersecurity
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