  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.
