Aiming to provide more efficient and robust physical layer security strategies for wireless communications, this article investigates the endogenous security of end-to-end learning of communication by addressing two main security issues of communication: confidential transmission and user authentication. For confidential transmission, we have redesigned the loss function of the autoencoder-based deep learning communication model to combat illegal eavesdropping over wireless broadcast channels. While assuming that the eavesdropper has three different ways of decoding prior information, the probability of successful eavesdropping attack is evaluated using the bit error rate criterion. In terms of user authentication, an authentication scheme using "symbol-level fingerprints" is designed for a single user, which takes advantage of the high complexity of parameters of the deep learning model and its natural sensitivity to training conditions. In addition, by leveraging a denoising autoencoder, we extend the authentication to adapt to the multi-user access situation. Experiments have shown that the proposed authentication scheme could guarantee reliability under dynamic channel and resistance to wireless attacks. The results inspire us to rebuild an efficient physical layer secure framework for wireless communication through a new deep learning method.

中文翻译：

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安全无线通信的端到端学习：机密传输和身份验证

为了为无线通信提供更有效和健壮的物理层安全策略，本文通过解决通信的两个主要安全问题：机密传输和用户身份验证，研究了端到端学习通信的内在安全性。为了进行机密传输，我们重新设计了基于自动编码器的深度学习通信模型的丢失功能，以打击无线广播信道上的非法窃听。虽然假设窃听者具有三种解码先验信息的方式，但是使用误码率标准评估成功窃听攻击的概率。在用户身份验证方面，为单个用户设计了使用“符号级指纹”的身份验证方案，这利用了深度学习模型参数的高度复杂性及其对训练条件的自然敏感性。此外，通过利用降噪自动编码器，我们扩展了身份验证以适应多用户访问情况。实验表明，所提出的认证方案可以保证动态信道下的可靠性和对无线攻击的抵抗力。这些结果激励我们通过一种新型的深度学习方法为无线通信重建高效的物理层安全框架。实验表明，所提出的认证方案可以保证动态信道下的可靠性和对无线攻击的抵抗力。这些结果激励我们通过一种新型的深度学习方法为无线通信重建高效的物理层安全框架。实验表明，所提出的认证方案可以保证动态信道下的可靠性和对无线攻击的抵抗力。这些结果激励我们通过一种新型的深度学习方法为无线通信重建高效的物理层安全框架。