Waveform-Defined Security: A Low-Cost Framework for Secure Communications

TL;DR

This paper introduces a waveform-defined security framework that enhances physical layer security without relying on channel conditions, making it suitable for resource-constrained systems and resistant to eavesdropping.

Contribution

The work proposes a novel waveform-based security method that is channel-independent and low-cost, with optimized waveform patterns and AI-based classifiers for secure communications.

Findings

WDS is more reliable than traditional PLS techniques.

Optimized waveform patterns significantly weaken eavesdropper signal classification.

Successful implementation in IEEE 802.11a and SDR experiments confirm feasibility.

Abstract

Communication security could be enhanced at physical layer but at the cost of complex algorithms and redundant hardware, which would render traditional physical layer security (PLS) techniques unsuitable for use with resource-constrained communication systems. This work investigates a waveform-defined security (WDS) framework, which differs fundamentally from traditional PLS techniques used in today's systems. The framework is not dependent on channel conditions such as signal power advantage and channel state information (CSI). Therefore, the framework is more reliable than channel dependent beamforming and artificial noise (AN) techniques. In addition, the framework is more than just increasing the cost of eavesdropping. By intentionally tuning waveform patterns to weaken signal feature diversity and enhance feature similarity, eavesdroppers will not be able to identify correctly signal formats. The wrong classification of signal formats would result in subsequent detection errors even when an eavesdropper uses brute-force detection techniques. To get a robust WDS framework, three impact factors, namely training data feature, oversampling factor and bandwidth compression factor (BCF) offset, are investigated. An optimal WDS waveform pattern is obtained at the end after a joint study of the three factors. To ensure a valid eavesdropping model, artificial intelligence (AI) dependent signal classifiers are designed followed by optimal performance achievable signal detectors. To show the compatibility in available communication systems, the WDS framework is successfully integrated in IEEE 802.11a with nearly no adding computational complexity. Finally, a low-cost software-defined radio (SDR) experiment is designed to verify the feasibility of the WDS framework in resource-constrained communications.