Malicious attacks in wireless cyber-physical systems have become more frequent in recent years. With the development of attack methods used by attackers, security in wireless cyber-physical systems needs to progress to match various attacks. Deep learning is a field that has developed rapidly in recent years, and generative adversarial network (GAN) is a deep learning model that has shown promising results. GAN has two interlocking subsystems, one to generate fake samples and another to classify the generated fake samples. Finally, the system has two well trained subsystems that are capable of both generating convincing samples and classifying generated samples. In this article, we propose a self-training powered GAN (ST-GAN) system to detect attacks in wireless cyber-physical systems. At the same time, the proposed ST-GAN system solves the issue of limited data in the field of security for wireless cyber-physical systems, which is caused by confidentiality as well as the number of attacks. Our experiments have shown that the proposed system can effectively detect attacks in wireless cyber-physical systems.

中文翻译：

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基于自训练驱动的生成对抗网络的无线网络物理系统的攻击检测和数据生成


近年来，无线网络物理系统中的恶意攻击变得更加频繁。随着攻击者使用的攻击方法的发展，无线信息物理系统的安全性需要不断进步以匹配各种攻击。深度学习是近年来快速发展的领域，生成对抗网络（GAN）是一种已经显示出可喜成果的深度学习模型。 GAN 有两个互锁的子系统，一个用于生成假样本，另一个用于对生成的假样本进行分类。最后，该系统具有两个训练有素的子系统，它们既能够生成令人信服的样本，又能对生成的样本进行分类。在本文中，我们提出了一种自训练驱动的 GAN (ST-GAN) 系统来检测无线网络物理系统中的攻击。同时，所提出的ST-GAN系统解决了无线信息物理系统安全领域中由于保密性和攻击数量而导致的数据有限的问题。我们的实验表明，所提出的系统可以有效地检测无线网络物理系统中的攻击。