Dilli Sharma's home page

Dilli P. Sharma, PhD

Researcher | Lecturer | Author

Cybersecurity • Artificial Intelligence • Explanable & Trustworthy AI • Privacy-Preserving AI 

Welcome to my homepage!

I am currently a Postdoctoral Researcher at York University, Toronto, Canada, and previously held similar positions at the University of Toronto and the University of New Brunswick, Canada. I earned my Ph.D. in Computer Science from the University of Canterbury, Christchurch, New Zealand.

My research lies at the intersection of Cybersecurity and Artificial Intelligence, with a focus on developing secure, trustworthy, and privacy-preserving intelligent systems. I am particularly interested in designing proactive defense mechanisms, robust machine learning models, and explainable AI solutions for real-world applications. 

Research Interests

• Cybersecurity 

• Trustworthy and Explainable AI (XAI)

• Privacy-Preserving Machine Learning

• Adversarial Machine Learning

• AI for Public Safety and Emergency Response

• Internet of Things (IoT) Security

• Security Metrics and Optimization

• Moving Target Defense (MTD)

• Responsible AI

My long-term goal is to advance secure, explainable, and privacy-aware AI technologies that improve the resilience, safety, and reliability of modern computing systems and critical infrastructure.

Recently Published/Accepted Papers/Books:

Geng, H., Beigi-Mohammadi, N., Sharma, D. P., Liu, J., & Leon-Garcia, A. (2026). City-wide Traffic Prediction Using Aggregated Mobile Network Activity Data. IEEE Open Journal of Intelligent Transportation Systems. https://doi.org/10.1109/OJITS.2026.3708478

Sun, X., Xue, H., Sharma, D. P., Xue, L., Lin, X., & Xiong, P. (2026). Navigating the Privacy-Explainability-Utility Trilemma: A Survey of Differential Privacy and Explainable AI. Journal of Information and Intelligence. https://doi.org/10.1016/j.jiixd.2026.05.005

Niktabe, S., Sharma, D. P., & Lashkari, A. H. (2026). Unveiling Intruders' Behaviors: Explainable AI-Based Profiling of Malicious Bot Activities in IoT Networks. The Journal of Supercomputing, 82, 352. https://doi.org/10.1007/s11227-026-08494-6

Sharma, D. P., Sun, X., Xue, L., Lin, X., & Xiong, P. (2025). Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization. In Proceedings of the IEEE Annual Congress on Artificial Intelligence of Things (IEEE AIoT 2025), Osaka, Japan, December 3–5, 2025. https: https://ieeexplore.ieee.org/document/11416471

Sharma, D. P., Xue, L., Sun, X., Lin, X., & Xiong, P. (2025). Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting. In Proceedings of the 24th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2025), Guiyang, China, November 14–17, 2025. https://ieeexplore.ieee.org/document/11354855

Sharma, D. P., Beigi-Mohammadi, N., Soni, P., Madro, R., Emmenegger, P., Tobar, C., Li, J., & Leon-Garcia, A. (2025). A Machine Learning Framework for Fire Risk Prediction with Response and Proximity Insights. IEEE Access, 13, 149620–149636. https://doi.org/10.1109/ACCESS.2025.3602259

Sharma, D. P. (2025). Evaluating Moving Target Defense Methods Using Time to Compromise and Security Risk Metrics in IoT Networks. Electronics, 14(11), 2205. https://doi.org/10.3390/electronics14112205

Sharma, D. P., Lashkari, A. H., Firoozjaei, M. D., Mahdavifar, S., & Xiong, P. (2025). Understanding AI in Cybersecurity and Secure AI: Challenges, Strategies and Trends. Springer Cham. ISBN: 978-3-031-91523-9. https://link.springer.com/book/10.1007/978-3-031-91524-6

Sharma, D. P., Lashkari, A. H., & Parizadeh, M. (2024). Understanding Cybersecurity Management in Healthcare: Challenges, Strategies and Trends. Springer Cham. ISBN: 978-3-031-68033-5. https://doi.org/10.1007/978-3-031-68034-2

Sharma, D. P., Beigi-Mohammadi, N., Geng, H., Dixon, D., Madro, R., Emmenegger, P., Tobar, C., Li, J., & Leon-Garcia, A. (2024). Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events in the City of Edmonton. IEEE Access, 12, 56880–56909. https://doi.org/10.1109/ACCESS.2024.3390089

He, X., Lashkari, A. H., Vombatkere, N., & Sharma, D. P. (2024). Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey. Information, 15(3), 131. https://doi.org/10.3390/info15030131

Niktabe, S., Lashkari, A. H., & Sharma, D. P. (2024). Detection, Characterization, and Profiling of DoH Malicious Traffic Using Statistical Pattern Recognition. International Journal of Information Security, 23, 1293–1316. https://doi.org/10.1007/s10207-023-00790-z

Sharma, D. P., Kaur, B., Shoeleh, F., Erfani, M., Le, D. P., Lashkari, A. H., & Ghorbani, A. A. (2021). Adaptive User Profiling with Online Incremental Machine Learning for Security Information and Event Management. In Proceedings of the 15th International Conference on Emerging Security Information, Systems and Technologies (SECURWARE 2021), Athens, Greece, pp. 82–87.

Sharma, D. P., Enoch, S. Y., Cho, J. H., Moore, T. J., Nelson, F. F., Lim, H., & Kim, D. S. (2020). Dynamic Security Metrics for Software-Defined Network-Based Moving Target Defense. Journal of Network and Computer Applications, 167, 102805. https://doi.org/10.1016/j.jnca.2020.102805

Cho, J. H., Sharma, D. P., Alavizadeh, H., Yoon, S. H., Ben-Asher, N., Moore, T. J., Kim, D. S., Lim, H., & Nelson, F. F. (2020). Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense. IEEE Communications Surveys & Tutorials, 22(1), 709–745. https://doi.org/10.1109/COMST.2019.2963791

Sharma, D. P., Cho, J. H., Moore, T. J., Nelson, F. F., Lim, H., & Kim, D. S. (2019). Random Host and Service Multiplexing for Moving Target Defense in Software-Defined Networks. In Proceedings of the IEEE International Conference on Communications (ICC 2019), Shanghai, China, pp. 1–6. https://doi.org/10.1109/ICC.2019.8761496

Dishington, C., Sharma, D. P., Kim, D. S., Cho, J. H., Moore, T. J., & Nelson, F. F. (2019). Security and Performance Assessment of IP Multiplexing Moving Target Defence in Software Defined Networks. In Proceedings of the 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications / 13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE 2019), Rotorua, New Zealand, pp. 288–295. https://doi.org/10.1109/TrustCom/BigDataSE.2019.00046

Sharma, D. P., Kim, D. S., Yoon, S. H., Lim, H., Cho, J. H., & Moore, T. J. (2018). FRVM: Flexible Random Virtual IP Multiplexing in Software-Defined Networks. In Proceedings of the 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications / 12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE 2018), New York, NY, USA, pp. 579–587. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00088

Click here to find the complete list of publications in Google Scholar