The widespread use of mobile devices, as well as the increasing popularity of mobile services has raised serious cybersecurity challenges. In the last years, the number of cyberattacks has grown dramatically, as well as their complexity. Traditional cybersecurity systems have failed to detect complex attacks, unknown malware, and they do not guarantee the preservation of user privacy. Consequently, cybersecurity systems have embraced Deep Learning (DL) models as they provide efficient detection of novel attacks and better accuracy. This paper presents a comprehensive survey of recent cybersecurity works that use DL in mobile and wireless networks. It covers all cybersecurity aspects: infrastructure threads and attacks, software attacks and privacy preservation. First, we provide a detailed overview of DL techniques applied, or with potential applications, to cybersecurity. Then, we review cybersecurity works based on DL. For each cybersecurity threat or attack, we discuss the challenges for using DL methods. For each contribution, we review the implementation details and the performance of the solution. In a nutshell, this paper constitutes the first survey that provides a complete review of the DL methods for cybersecurity. Given the analysis performed, we identify the most effective DL methods for the different threats and attacks.

中文翻译：

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移动网络网络安全深度学习技术综述


移动设备的广泛使用以及移动服务的日益普及带来了严峻的网络安全挑战。在过去几年中，网络攻击的数量及其复杂性急剧增加。传统的网络安全系统无法检测复杂的攻击、未知的恶意软件，并且不能保证用户隐私的保护。因此，网络安全系统已经采用了深度学习（DL）模型，因为它们可以有效检测新型攻击并提高准确性。本文对最近在移动和无线网络中使用深度学习的网络安全工作进行了全面调查。它涵盖了所有网络安全方面：基础设施线程和攻击、软件攻击和隐私保护。首先，我们详细概述了网络安全中应用的深度学习技术或潜在应用。然后，我们回顾了基于深度学习的网络安全工作。对于每种网络安全威胁或攻击，我们讨论使用深度学习方法所面临的挑战。对于每项贡献，我们都会审查解决方案的实施细节和性能。简而言之，本文是第一份对网络安全深度学习方法进行完整回顾的调查。根据所进行的分析，我们确定了针对不同威胁和攻击的最有效的深度学习方法。