Graph Layer Security: Encrypting Information via Common Networked Physics.

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. Whilst recently developed physical layer security (PLS) can exploit common wireless channel state information (CSI), its sensitivity to channel estimation makes them vulnerable to 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 characterise such dependency into a graph-bandlimited subspace, which allows the generation of channel-irrelevant cipher keys by maximising 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.

Simultaneous channel estimation and signal detection in wireless ultraviolet communications combating inter-symbol-interference.

One primary challenge in wireless ultraviolet communications (UVCs) is the inter-symbol-interference (ISI), which may block the detection of current informative signal, especially when channel-related characteristics are unknown. In this paper, we propose a UV channel-related Bayesian scheme that can simultaneously estimate the channel characteristics and detect informative signals, which therefore can address the ISI disturbance. By investigating the UV single-scattering photon model, the dynamic behaviors of the channel state information (CSI), which involve the uncertain signal and the unknown channel parameters are formulated. Hence, a sequential Bayesian process is suggested to estimate the UV CSI. Numerical analysis shows that the proposed scheme can obtain a promising estimation performance (i.e., the relative errors are less than 4%), and gain an extra 4dB detection performance compared with imperfect maximum-likelihood sequence detection (MLSD) scheme.

Review of Physical Layer Security in Molecular Internet of Nano-Things.

Molecular networking has been identified as a key enabling technology for Internet-of-Nano-Things (IoNT): microscopic devices that can monitor, process information, and take action in a wide range of medical applications. As the research matures into prototypes, the cybersecurity challenges of molecular networking are now being researched on at both the cryptographic and physical layer level. Due to the limited computation capabilities of IoNT devices, physical layer security (PLS) is of particular interest. As PLS leverages on channel physics and physical signal attributes, the fact that molecular signals differ significantly from radio frequency signals and propagation means new signal processing methods and hardware is needed. Here, we review new vectors of attack and new methods of PLS, focusing on 3 areas: (1) information theoretical secrecy bounds for molecular communications, (2) key-less steering and decentralized key-based PLS methods, and (3) new methods of achieving encoding and encryption through bio-molecular compounds. The review will also include prototype demonstrations from our own lab that will inform future research and related standardization efforts.