Classifier selection for physical layer security augmentation in Cognitive Radio networks

Abstract

Cognitive Radio (CR) networks create an environment that presents unique security challenges, with reliable user authentication being essential for mitigating Primary User Emulation (PUE) spoofing and ensuring the cognition engine is using reliable information when dynamically reconfiguring the network. Unfortunately, wireless network edge devices increase spoofing potential as all devices can “see” all network traffic within RF range. Conventional bit-level security helps, but additional security based on physical-layer (PHY) attributes is required to ensure unauthorized devices do not adversely impact CR reliability during environmental assessment. RF Distinct Native Attribute (RF-DNA) fingerprinting is one PHY technique for reliably identifying devices based on inherent emission differences. These differences are exploited to uniquely identify, by serial number, hardware devices and aid cognitive network security. Reliable device discrimination has been achieved using Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) processing. However, MDA/ML provides no insight into feature relevance which limits its use for optimizing feature selection. This limitation is addressed here using Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) and Learning from Signals (LFS) classifiers. Comparative assessment shows that GRLVQI and LFS classification performance rivals that of MDA/ML, overcomes inherent MDA/ML limitations, and provides benefit for CR network applications where reliable RF environment assessment and PUE mitigation is essential.