Computing Science, Simon Fraser University
Email: tayebi@sfu.ca
Office: ASB 9931

Mohammad Tayebi

Assistant Professor (Practitioner)

I am an Assistant Professor (Practitioner) in the School of Computing Science at Simon Fraser University, where I co-chair the Professional Master's Program in Cybersecurity. My research focuses on applying machine learning to advance cybersecurity and public safety, with a strong emphasis on real-world challenges. My work in public safety includes areas such as predictive policing, maritime safety and fraud detection. In cybersecurity, I am particularly interested in securing critical infrastructure and exploring the potential of large language models for identifying and mitigating vulnerabilities, as well as for threat intelligence.



Supervision

  • Mohsen Iranmanesh, Master's Student
  • Kourosh Hashemi, Undergraduate Student
  • Erfan Shafagh, Undergraduate Student
  • Soroush Motamedi, Undergraduate Student

Teaching

  • CMPT 782 – Cybersecurity Lab1 2020-Present
  • CMPT 785 – Secure Software Design 2023-Present

Recent Publications

  • O. Tsai, J. Li, T. T. Cheung, L. Huang, H. Zhu, J. Xiao, I. Sharafaldin, and M. A. Tayebi, GraphQLer: Enhancing GraphQL Security with Context-Aware API Testing, arXiv preprint arXiv:2504.13358, 2025. https://arxiv.org/abs/2504.13358
  • Z. Wang and M. A. Tayebi, AutoRed: Automated Attack Scenario Generation Framework for Red Teaming of LLMs. 2024 IEEE International Conference on Big Data (BigData'24), pp. 2376-2383, Washington DC, USA, 2024.
  • D. Leung, O. Tsai, K. Hashemi, B. Tayebi, and M. A. Tayebi, XploitSQL: Advancing Adversarial SQL Injection Attack Generation with Language Models and Reinforcement Learning. 33rd ACM International Conference on Information and Knowledge Management (CIKM'24), pp. 4653-4660, Boise, USA, 2024.
  • M. Isakhani, M. Huang, M. A. Tayebi, and A. Habibi Lashkari. An Evolutionary Algorithm for Adversarial SQL Injection Attack Generation. 2023 IEEE Intelligence and Security Informatics (ISI'23), pp. 1-6, 2023.
  • S. Haolun, M. A. Tayebi, J. Pei, and J. Cao. Cost-Sensitive Learning for Medical Insurance Fraud Detection with Temporal Information. IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(10): pp. 10451-10463, 2023.
  • M. Keramati, M. A. Tayebi, Z. Zohrevand, U. Glaesser, J. Anzieta, and G. Williams-Jones. Cubism: Co-Balanced Mixup for Unsupervised Volcano-Seismic Knowledge Transfer. 2022 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'22), pp 581-597, Grenoble, France, 2022.
  • A. Y. Shahir, T. Charalampous, M. A. Tayebi, U. Glaesser, and H. Wehn. TripTracker: Unsupervised Learning of Fishing Vessel Routine Activity Patterns. 2021 IEEE International Conference on Big Data (BigData'21), pp. 1928-1939, 2021.
  • S. Bolourani, M. A. Tayebi, L. Diao, P. Wang, V. Patel, F. Manetta, P. C. Lee. Using machine learning to predict early readmission following esophagectomy. The Journal of Thoracic and Cardiovascular Surgery, 161(6), 1926-1939, 2021.
  • S. Arasteh, M. A. Tayebi, Z. Zohrevand, U. Glaesser, A. Y. Shahir, P. Saeedi, H. Wehn, Fishing Vessels Activity Detection from Longitudinal AIS Data, 28th International Conference on Advances in Geographic Information Systems (SIGSPATIAL '20), pp. 347-356, Seattle, USA, 2020.