The number of Internet of Things (IoT) deployments is expected to reach 75.4 billion by 2025. Roughly 70% of all IoT devices employ weak or no encryption; thus, putting them and their connected infrastructure at risk of attack by devices that are wrongly authenticated or not authenticated at all. A physical layer security approach -- known as Specific Emitter Identification (SEI) -- has been proposed and is being pursued as a viable IoT security mechanism. SEI is advantageous because it is a passive technique that exploits inherent and distinct features that are unintentionally added to the signal by the IoT Radio Frequency (RF) front-end. SEI's passive exploitation of unintentional signal features removes any need to modify the IoT device, which makes it ideal for existing and future IoT deployments. Despite the amount of SEI research conducted, some challenges must be addressed to make SEI a viable IoT security approach. One challenge is the extraction of SEI features from signals collected under multipath fading conditions. Multipath corrupts the inherent SEI features that are used to discriminate one IoT device from another; thus, degrading authentication performance and increasing the chance of attack. This work presents two semi-supervised Deep Learning (DL) equalization approaches and compares their performance with the current state of the art. The two approaches are the Conditional Generative Adversarial Network (CGAN) and Joint Convolutional Auto-Encoder and Convolutional Neural Network (JCAECNN). Both approaches learn the channel distribution to enable multipath correction while simultaneously preserving the SEI exploited features. CGAN and JCAECNN performance is assessed using a Rayleigh fading channel under degrading SNR, up to thirty-two IoT devices, and two publicly available signal sets. The JCAECNN improves SEI performance by 10% beyond that of the current state of the art.

翻译：物联网（IoT）部署数量预计到2025年将达到754亿。约70%的物联网设备采用弱加密或无加密，因此使其及其连接的基础设施面临被错误认证或完全未认证设备攻击的风险。一种物理层安全方法——称为特定发射机识别（SEI）——已被提出并作为可行的物联网安全机制进行研究。SEI的优势在于它是一种被动技术，利用物联网射频（RF）前端无意中添加到信号中的固有独特特征。SEI对这些非故意信号特征的被动利用消除了修改物联网设备的任何需求，使其非常适合现有和未来的物联网部署。尽管进行了大量SEI研究，但要使SEI成为一种可行的物联网安全方法，仍需解决一些挑战。其中一个挑战是从多径衰落条件下收集的信号中提取SEI特征。多径会破坏用于区分不同物联网设备的固有SEI特征，从而降低认证性能并增加攻击风险。本文提出了两种半监督深度学习（DL）均衡方法，并将其性能与当前最先进技术进行了比较。这两种方法是条件生成对抗网络（CGAN）和联合卷积自编码器与卷积神经网络（JCAECNN）。两种方法都学习信道分布以实现多径校正，同时保留SEI利用的特征。通过瑞利衰落信道在信噪比（SNR）恶化条件下、最多三十二个物联网设备以及两个公开可用信号集上评估了CGAN和JCAECNN的性能。JCAECNN将SEI性能比当前最先进技术提升了10%。