Machine Learning for Physical Layer Security

信息处理的数字化使信息几乎随时随地可用，从而颠覆性地改变了每个人的生活。随之而来的是对频谱高效(无线)通信系统的需求，特别是复杂的安全机制，以确保通信免受对手攻击，并保护数据和用户的隐私。目前，与安全相关的任务在更高层实现，通常基于加密原理。它们有广泛的用途，并基于对手计算能力不足和某些问题的计算难度的假设。然而，由于计算能力的增强、算法的改进以及数论的最新进展，这些方法变得越来越不安全。最近，作为对密码技术的补充，物理层安全或信息理论安全的概念已被研究。这样的方法通过利用通信信道的物理属性在物理层联合建立可靠的通信和无条件安全。然而，由于其对任意和变化的网络配置和信道条件的普适性等挑战，实际实现仍然处于起步阶段。在另一项工作中，已经证明，使用机器学习工具的通信系统可以学习快速可靠的通信方案，特别是通过使用所谓的深度神经网络(深度学习)。这样的机器学习工具可以帮助解决通信理论面临的一些挑战，并提供一种方法来设计复杂的通信系统，这些系统不需要手动调整到特定的信道条件，但可以灵活地适用于广泛的场景。本提案解决了为安全(无线)通信系统开发基于机器学习的技术的挑战。该提案的核心是三个关键点：第一个目标将是识别、调查和制定合适的安全指标。需要选择和设计这样的指标，以便它们保持其安全的操作意义，并进一步允许将不同的学习算法纳入培训。第二个目标是在深度学习框架内开发物理层安全模型。本部分将介绍编码和通信的最新发展，并扩展这些物理层安全模型。第三个目标是研究更一般的深度学习概念，包括强化学习、递归神经网络和生成性对抗网络，并有效地为现实世界场景实施由此产生的技术和算法。

The digitalization of information processing disruptively changes everyone's life by making information available almost everywhere at any time. With this comes the need of spectrally efficient (wireless) communication systems and, in particular, sophisticated security mechanisms that secure the communication against adversarial attacks and protect the privacy of the data and users.Security related tasks are currently realized on higher layers and usually based on cryptographic principles. These have a wide variety of use and are based on the assumption of insufficient computational capabilities of adversaries and computational hardness of certain problems. However, due to increasing computational power, improved algorithms, and recent advances in number theory, these approaches are becoming less and less secure. Recently, the concept of physical layer security or information theoretic security has been examined as a complement to cryptographic techniques. Such approaches establish reliable communication and unconditional security jointly at the physical layer by exploiting physical properties of the communication channel. However, practical implementations are still in its infancy due to challenges such as its generalizability to arbitrary and changing network configurations and channel conditions.In another line of work, it has been demonstrated that fast and reliable communication schemes can be learned by communication systems using machine learning tools; particularly by using so-called deep neural networks (deep learning). Such machine learning tools can help to solve some of the challenges the communication theory is faced with and provide a way to design sophisticated communication systems that do not need to be tuned by hand to specific channel conditions but are flexible and applicable to a broad range of scenarios. The present proposal tackles the challenge of developing machine learning based echniques for secure (wireless) communication systems. The core of the proposal are three key points:The first goal will be to identify, investigate, and to develop suitable security metrics. Such metrics need to be chosen and designed such that they keep their operational meaning of security and further allow the incorporation into training of different learning algorithms. The second goal will be to develop physical layer security models within the deep learning framework. This part will embrace recent developments for coding and communication and expand those models for physical layer security. The third goal will be to look into more general deep learning concepts including reinforcement learning, recurrent neural networks and generative adversarial networks and efficiently implement the resulting techniques and algorithms for real world scenarios.