Spatial de-correlation of generated keys from wireless channels using adversarial deep learning

IF 2.4 4区计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Information Security Pub Date : 2024-03-15 DOI:10.1007/s10207-024-00831-1

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Abstract

Physical-layer Key Generation (PKG) is the major candidate for use in secure wireless communications and Internet of Things (IoT) systems. Using Deep Learning (DL) and the Band Feature Mapping (BFM) method leads to reciprocal features, which is an essential requirement for the key generation in Orthogonal Frequency-Division Multiplexing Frequency Division Duplexing systems. Additionally, randomness and spatial de-correlation are two other essential requirements of secure PKG schemes. When the distance of an eavesdropper from a legal user is short, the eavesdropper can experience a correlated fading and generate the secret key.Other works assume that the adversary is far away from legitimate users, whereas the proposed scheme allows the adversary to approach the legitimate users without sacrificing the security Conventional DL-based BFM includes an offline training stage using a pre-collected dataset. To solve the spatial correlation problem, this paper simultaneously uses the concepts of physical layer security and adversarial training. Moreover, a DL-based adversary in the PKG model is considered which has not been studied yet. Simulation results confirm the effectiveness of the proposed Adversarial DL (ADL) key generation scheme in terms of Key Error Rate and Key Generation Rate. Our results show that using the proposed training strategy the illegal user can only generate a random key with an error rate of about 0.5. In the meantime, this method maintains the performance of the generated key by the legal users under a certain level. The mentioned features make ADL key generation scheme an appealing candidate for applications, such as secure cloud-based communications, low-size networks, and resource-constrained IoT.

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利用对抗性深度学习消除无线信道生成密钥的空间相关性

摘要物理层密钥生成（PKG）是安全无线通信和物联网（IoT）系统的主要候选技术。使用深度学习（DL）和频带特征映射（BFM）方法可生成互易特征，这是正交频分复用（Orthogonal Frequency-Division Multiplexing）频分复用系统密钥生成的基本要求。此外，随机性和空间去相关性也是安全 PKG 方案的两个基本要求。当窃听者与合法用户的距离很短时，窃听者可以经历相关性衰减并生成密钥。其他作品假定对手远离合法用户，而所提出的方案允许对手接近合法用户而不影响安全性。为了解决空间相关性问题，本文同时使用了物理层安全和对抗训练的概念。此外，本文还考虑了 PKG 模型中基于 DL 的对手，这一点目前还没有研究。仿真结果证实了所提出的对抗式 DL（ADL）密钥生成方案在密钥错误率和密钥生成率方面的有效性。我们的结果表明，使用所提出的训练策略，非法用户只能生成错误率约为 0.5 的随机密钥。同时，这种方法还能将合法用户生成密钥的性能保持在一定水平之下。上述特点使 ADL 密钥生成方案成为基于云的安全通信、低规模网络和资源受限的物联网等应用的理想候选方案。

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来源期刊

International Journal of Information Security 工程技术-计算机：理论方法

CiteScore

6.30

自引率

3.10%

发文量

审稿时长

12 months

期刊介绍： The International Journal of Information Security is an English language periodical on research in information security which offers prompt publication of important technical work, whether theoretical, applicable, or related to implementation. Coverage includes system security: intrusion detection, secure end systems, secure operating systems, database security, security infrastructures, security evaluation; network security: Internet security, firewalls, mobile security, security agents, protocols, anti-virus and anti-hacker measures; content protection: watermarking, software protection, tamper resistant software; applications: electronic commerce, government, health, telecommunications, mobility.