IN-CYPHER (Imperial/NTU CYber Protection for HEalthcaRe)

IN-CYPHER is Imperial Global Singapore’s inaugural research programme in collaboration with Nanyang Technological University (NTU), aims to leverage the complementary skills at Imperial and NTU to undertake world-leading research and establish Singapore as a leader in health cybersecurity and AI for healthcare.

Technology advances have increased the connectivity of diagnostic, therapeutic and implantable devices, and supported the aggregation of the data they produce. This brings both opportunities and challenges: opportunities to use insights gained from data to improve the performance of medical devices and challenges of protecting both data and device against adversarial attack.

To address these challenges, the IN-CYPHER research and innovation programme is focused on security, privacy and trust around healthcare technologies along four themes:

  • Theme 1 Protecting Implantable Active Devices
  • Theme 2 Securing Connected Wearables and Healthcare Systems
  • Theme 3 Algorithms for Security, Privacy and Provenance
  • Theme 4 Clinical Innovation & Translation

The overarching aim is to develop public trust in the security of emerging, future and connected medical devices to support the appropriate use of data for population-level improvements in healthcare.

The collaborating Departments include senior academics from the Department of Metabolism, Digestion and Reproduction and the Department of Bioengineering at Imperial – the largest Biomedical Engineering Department in the UK – and senior researchers from the School of Computer Science and Engineering (SCSE), School of Electrical and Electronic Engineering and the Lee Kong Chian School of Medicine at Nanyang Technological University. 

Publications

Binici, K., Kashyap, A. R., Schlegel, V., Liu, A. T., Dwivedi, V. P., Nguyen, T. T., ... & Winkler, S. (2025, April). MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 22, pp. 23496-23504).
Bhuiya, N., Schlegel, V., & Winkler, S. (2024). Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 2514-2528).
C. Hwang, B. Zhou, Y. Ke, Mohan, V. et al., (2025). "Event-Based Neural Spike Detection Using Spiking Neural Networks for Neuromorphic iBMI Systems". In Proceedings of IEEE International Conference on Circuits and Systems (ISCAS), London, UK 2025. 
Chen, Z., Li, X., Zhang, J. M., Sarro, F., & Liu, Y. (2025, May). Diversity drives fairness: Ensemble of higher order mutants for intersectional fairness of machine learning software. In 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE) (pp. 743–755). IEEE Computer Society.
Deb, S., Lupu, E., Drakakis, E. M., Bharath, A. A., Leung, Z. K., Ma, G. R., & Chattopadhyay, A. (2025, July 25). Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures. arXiv
Dwivedi, V. P., Schlegel, V., Liu, A. T., Nguyen, T. T., Kashyap, A. R., Wei, J., ... & Tan, R. T. Representation Learning of Structured Data for Medical Foundation Models. In Proceedings of UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models.
G. Rajendran, D. Basak, S. Deb, and A. Chattopadhyay, “Securing binarized neural networks via puf-based key management in memristive crossbar arrays,” IEEE Embedded Systems Letters, 2024.
Jiang, J., Zheng, Y., Chang., C.H. (2025), "PUF-Based Edge DNN Model IP Protection with Self-Obfuscation and Publicly Verifiable Ownership". Accepted, to appear IEEE International Conference on Circuits and Systems (ISCAS), London, UK 2025. 
Jiao, R., Zheng, Y., Ghajari, S., Sadeghi, S., Cortese, A., McEuen, P., Molnar, A., & Lee, S. (2025). A 43 µm × 269 µm, light-tolerant and power-adaptive forward-bulk optoelectrical microsystem for tetherless neural recording (pp. 1–5). IEEE.
Kong, Z., Zhang, C., Xie, M., Hu, M., Xue, Y., Liu, Y., ... & Liu, Y. (2025). Smart Contract Fuzzing Towards Profitable Vulnerabilities. Proceedings of the ACM on Software Engineering, 2(FSE), 153-175.
Li, H., Huang, Y.-H., Xu, C., Schlegel, V., Jiang, R.-H., Batista-Navarro, R., Nenadic, G., & Bian, J. (2025). BRIDGE: Bootstrapping text to control time-series generation via multi-agent iterative optimization and diffusion modelling. In Proceedings of the 42nd International Conference on Machine Learning. PMLR.
Liang, R., Chen, J., Wu, C., He, K., Wu, Y., Cao, R., ... & Liu, Y. (2025). Vulseye: Detect smart contract vulnerabilities via stateful directed graybox fuzzing. IEEE Transactions on Information Forensics and Security.
Liang, R., Chen, J., Wu, C., He, K., Wu, Y., Sun, W., ... & Liu, Y. (2025). Towards effective detection of ponzi schemes on ethereum with contract runtime behavior graph. ACM Transactions on Software Engineering and Methodology, 34(4), 1-32.
Liu, Y., Xue, Y., Wu, D., Sun, Y., Li, Y., Shi, M., & Liu, Y. (2025, February). PropertyGPT: LLM‑driven formal verification of smart contracts through retrieval‑augmented property generation. In Proceedings of the 32nd Annual Network and Distributed System Security Symposium (NDSS).
Luan, X., Sanan, D., Hou, Z., Xu, Q., Liu, C., Cai, Y., ... & Sun, M. (2025). Why the Proof Fails in Different Versions of Theorem Provers: An Empirical Study of Compatibility Issues in Isabelle. Proceedings of the ACM on Software Engineering, 2(FSE), 1499-1521.
Madusanka, T., Valentino, M., Zahid, I., Pratt-Hartmann, I., & Batista-Navarro, R. (2025). Unravelling the logic: Investigating the generalisation of transformers in numerical satisfiability problems. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 25155–25168). Association for Computational Linguistics.
Mohan, V., Zhou, B. and Wang, Z. (2025) "Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI". In Proceedings of IEEE International Conference on Circuits and Systems (ISCAS), London, UK 2025. 
Mohan. V., Tay, W.P. and Basu, A. (2025), "Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface". Neuromorphic Computing and Engineering. 5 014004, Jan 2025. 
Nagar, A., Liu, Y., Liu, A. T., Schlegel, V., Dwivedi, V. P., Kaliya-Perumal, A. K., ... & Tan, R. T. (2025). uMedSum: A Unified Framework for Advancing Medical Abstractive Summarization. Forthcoming in Proceedings of the 63rd Conference of  the Association for Computational Linguistics.
Pöhl, P., Schlegel, V., Li, H., & Bharath, A. (2025). Generating realistic multi-beat ECG signals. Forthcoming In Proceedings of the 25th International Conference on Digital Signal Processing. IEEE.
V. Mohan, G. Sun, V. Schlegel, T. Dai, S. Tukra, and A. A. Bharath, “The Science of AI: Probing Activations in Deep Networks,” in Proc. AI4X, Apr. 2025.
Wang Z., Mohan V., et al., (2025) "RSPE A High Energy Efficient SNN Inference Processor with RISC-V based Dynamic Pruning Mechanism". To Appear, IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Bordeaux, France. 
Wang, Z., Du, H., Tang, X., Qiao, S., Yin, S., Bharath, A. and Drakakis, M. (2025) "MPE: A power-efficient edge-device Mamba processor with multi-dimensional calculation-compression scheme"
Wang, Z., Du, H., Zhou, J., Xu, Y., Tang, X., Ye, T., ... & Yin, S. (2024, November). SSPE: A Device-edge SNN Inference Artificial Intelligence Processor in Supporting Smart Computing. In 2024 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) (pp. 120-124). IEEE.
Wang, Z., Du, H., Zhou, J., Zhou, Y., Tang, X., Ye, T., ... & Yin, S. (2024, November). GRS: A General RISC-V SIMD Vector Acceleration Processor for Artificial Intelligence Applications. In 2024 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) (pp. 702-706). IEEE.
Wang, Z., H. Du, J. Zhou, et al., (2025), "GPE A High-Performance Edge GNN Inference Processor with Multi-Parallelism Format-Variation Mechanism". To Appear, IEEE International Symposium on Circuits and Systems (ISCAS), London, UK. 
Wang, Z., H. Du, Mohan, V., et al., (2025) "STPE An Energy-Efficient Edge-Device Transformer Inference Processor with Multi-Mode Data-Compression Scheme". To appear: IEEE International Symposium on Circuits and Systems (ISCAS), London, UK. 
Wang, Z., H. Du, Y. Xu, et al., (2025) "UPE A Device-edge DNN Inference Artificial Intelligence Processor with Supporting Reconfigurable Training". To Appear, IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Bordeaux, France. 
Wu, C., Sun, J., Chen, J., Alazab, M., Liu, Y., & Xiang, Y. (2025). TCG-IDS: Robust Network Intrusion Detection via Temporal Contrastive Graph Learning. IEEE Transactions on Information Forensics and Security.
Y. Zheng, C.-H. Chang, S.-H. Huang, P. Chen, and S. Picek, “An Overview of Trustworthy AI: Advances in IP Protection, Privacy-Preserving Federated Learning, Security Verification, and GAI Safety Alignment,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. PP, pp. 1–1, 2024.
Zahid, I., Sun, Y., & Batista-Navarro, R. (2025). BEDAA: Bayesian enhanced DeBERTa for uncertainty-aware authorship attribution. In Findings of the Association for Computational Linguistics: ACL 2025 (pp. 17952–17966). Association for Computational Linguistics.
Zhang, L., Liu, C., Wu, J., Zhang, S., Liu, C., Xu, Z., ... & Liu, Y. (2025). Drop the Golden Apples: Identifying Third-Party Reuse by DB-Less Software Composition Analysis. arXiv preprint arXiv:2503.22576.
Zhang, X., Zhai, J., Ma, S., Shen, C., Li, T., Jiang, W., & Liu, Y. STAFF: Speculative Coreset Selection for Task-Specific Fine-tuning. In The Thirteenth International Conference on Learning Representations.

Contact us

Paige Noyce 
Associate Director
p.noyce@imperial.ac.uk

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#11-01, CREATE Tower
Singapore 138602