Browsing by Author "Sun, Schyler Chengyao"

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    Graph layer security: encrypting information via common networked physics
    (MDPI, 2022-05-23) Wei, Zhuangkun; Wang, Liang; Sun, Schyler Chengyao; Li, Bin; Guo, Weisi
    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.
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    Trustworthy deep learning in 6G-enabled mass autonomy: from concept to quality-of-trust key performance indicators
    (IEEE, 2020-09-30) Li, Chen; Guo, Weisi; Sun, Schyler Chengyao; Al-Rubaye, Saba; Tsourdos, Antonios
    Mass autonomy promises to revolutionize a wide range of engineering, service, and mobility industries. Coordinating complex communication among hyperdense autonomous agents requires new artificial intelligence (AI)-enabled orchestration of wireless communication services beyond 5G and 6G mobile networks. In particular, safety and mission-critical tasks will legally require both transparent AI decision processes and quantifiable quality-of-trust (QoT) metrics for a range of human end users (consumer, engineer, and legal). We outline the concept of trustworthy autonomy for 6G, including essential elements such as how explainable AI (XAI) can generate the qualitative and quantitative modalities of trust. We also provide XAI test protocols for integration with radio resource management and associated key performance indicators (KPIs) for trust. The research directions proposed will enable researchers to start testing existing AI optimization algorithms and develop new ones with the view that trust and transparency should be built in from the design through the testing phase.