Dr Leishi Zhang


NameDr Leishi Zhang
Job titleReader in Computing
Research instituteSchool of Engineering, Technology and Design
ORCIDhttps://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-iot58464.2023.10539376

Machine 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. 140-153

A 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. 1074-1078

The 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/10.1177/1071181322661092

An 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/SMC52423.2021.9659095

Automated 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/10.1177/1071181321651348

Developing 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/10.1145/3444369

A 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.
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