Dr Leishi Zhang
| Name | Dr Leishi Zhang |
|---|---|
| Job title | Reader in Computing |
| Research institute | School of Engineering, Technology and Design |
| ORCID | https://orcid.org/0000-0002-3158-2328 (unauthenticated) |
Research outputs
Investigating security issues (multilayer attacks) on IoT devices using machine learning
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2024. Investigating security issues (multilayer attacks) on IoT devices using machine learning.Safeguarding IoMT: Semi-automated Intrusion Detection System (SAIDS) for detecting multilayer attacks
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2024. Safeguarding IoMT: Semi-automated Intrusion Detection System (SAIDS) for detecting multilayer attacks.Exploring optimal set of features in machine learning for improving IoT multilayer security
Al Sukhni, B., Manna, S., Dave, J. and Zhang, Leishi 2023. Exploring optimal set of features in machine learning for improving IoT multilayer security. 2023 IEEE 9th World Forum on Internet of Things (WF-IoT). https://doi.org/10.1109/wf-iot58464.2023.10539376Machine learning-based solutions for securing IoT systems against multilayer attacks
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Machine learning-based solutions for securing IoT systems against multilayer attacks. in: Singh Tomar, R., Verma, S., Kumar Chaurasia, B., Singh, V., Abawajy, J. H., Akashe, S., Hsiung, Pao-Ann and Prasad, R. (ed.) Communication, Networks and Computing Third International Conference, CNC 2022, Gwalior, India, December 8–10, 2022, Proceedings, Part I Cham Springer. pp. 140-153A review of privacy-preserving federated learning, deep learning, and machine learning IIoT and IoTs solutions
Obarafor, Victor, Qi, Man and Zhang, L. 2023. A review of privacy-preserving federated learning, deep learning, and machine learning IIoT and IoTs solutions. in: 2023 8th IEEE International Conference on Signal and Image Processing (ICSIP) Wuxi, China IEEE. pp. 1074-1078The impact of system transparency on analytical reasoning
Hepenstal, S., Zhang, L. and Wong, B.L.W. 2023. The impact of system transparency on analytical reasoning.The impact of system transparency on analytical reasoning
Hepenstal, S., Zhang, L. and Wong, B. 2023. The impact of system transparency on analytical reasoning. in: CHI '23: CHI Conference on Human Factors in Computing Systems, Hamburg Germany, April 23 - 28, 2023 New York ACM.Investigating the security issues of multi-layer IoMT attacks using machine learning techniques
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Investigating the security issues of multi-layer IoMT attacks using machine learning techniques.Designing a system to mimic expert cognition: An initial prototype
Hepenstal, Sam, Zhang, Leishi and William Wong, B. L. 2022. Designing a system to mimic expert cognition: An initial prototype. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 66 (1), pp. 2057-2061. https://doi.org/10.1177/1071181322661092An analysis of expertise in intelligence analysis to support the design of human-centered artificial intelligence
Hepenstal, S., Zhang, L. and Wong, BL William 2021. An analysis of expertise in intelligence analysis to support the design of human-centered artificial intelligence. https://doi.org/10.1109/SMC52423.2021.9659095Automated identification of insight seeking behaviours, strategies and rules: a preliminary study
Hepenstal, S., Zhang, L. and Wong, BL William 2021. Automated identification of insight seeking behaviours, strategies and rules: a preliminary study. Sage Journals: Proceedings of the Human Factors and Ergonomics Society Annual Meeting . (65), pp. 1269-1273. https://doi.org/https://doi.org/10.1177/1071181321651348Developing conversational agents for use in criminal investigations
Hepenstal, S., Zhang, L., Kodagoda N. and Wong B.L.W 2021. Developing conversational agents for use in criminal investigations. ACM Transactions on Interactive Intelligent Systems. 11 (3-4), pp. 1-35. https://doi.org/10.1145/3444369A granular computing approach to provide transparency of intelligent systems for criminal investigations
Zhang, L. 2021. A granular computing approach to provide transparency of intelligent systems for criminal investigations. in: Pedrycz, W. and Chen, S.-M. (ed.) Interpretable Artificial Intelligence: A Perspective of Granular Computing Cham Springer.1177
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