Graph Layer Security: Encrypting Information via Common Networked Physics

TL;DR

This paper introduces graph layer security (GLS), a novel encryption method leveraging shared physical network dynamics among IoT devices, providing security independent of wireless channel information and resilient against certain attacks.

Contribution

It proposes the first use of physical networked dynamics for encryption, utilizing graph Fourier transforms to generate secure keys without relying on wireless CSI.

Findings

GLS effectively encrypts information using network dynamics.

GLS resists attacks with partial network knowledge.

It outperforms traditional CSI-based security methods.

Abstract

The proliferation of low-cost Internet of Things (IoT) devices has led to a race between wireless security and channel attacks. Traditional cryptography requires high-computational power and is not suitable for low-power IoT scenarios. Whist, recently developed physical layer security (PLS) can exploit common wireless channel state information (CSI), its sensitivity to channel estimation makes them vulnerable from attacks. In this work, we exploit an alternative common physics shared between IoT transceivers: the monitored channel-irrelevant physical networked dynamics (e.g., water/oil/gas/electrical signal-flows). Leveraging this, we propose for the first time, graph layer security (GLS), by exploiting the dependency in physical dynamics among network nodes for information encryption and decryption. A graph Fourier transform (GFT) operator is used to characterize such dependency into a graph-bandlimted subspace, which allows the generations of channel-irrelevant cipher keys by maximizing the secrecy rate. We evaluate our GLS against designed active and passive attackers, using IEEE 39-Bus system. Results demonstrate that, GLS is not reliant on wireless CSI, and can combat attackers that have partial networked dynamic knowledge (realistic access to full dynamic and critical nodes remains challenging). We believe this novel GLS has widespread applicability in secure health monitoring and for Digital Twins in adversarial radio environments.