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Securing Smart Environments

Summary

Smart cities depend on connected devices—like intelligent thermostats, solar-integrated meters, and environmental sensors—to manage buildings, transit, energy use and public spaces. These tools promise greater efficiency, real-time monitoring, and better long-term planning. But while they’re transforming how cities operate, they also create serious cybersecurity and privacy risks. 

Many are built with limited security features, and manufacturers don’t always treat cybersecurity as a priority. At the same time, it’s hard to keep these devices updated when they’re deployed in the field for years at a time.

This project is tackling this challenge by developing tools and strategies to secure smart devices and protect the data they collect. The work focuses on the devices themselves, the privacy of users, and the expectations of people who operate and interact with them. Using technologies like generative AI, next-generation cryptography and advanced software analysis, this project aims to help cities embrace smart environments with confidence and resilience.

Key details

Principal investigator Lorenzo De Carli, University of Calgary
Co-principal investigators Mourad Debabbi, Concordia University  
Carol Fung, Concordia University  
Israat Haque, Dalhousie University 
Hadis Karimipour, University of Calgary 
Atefeh (Atty) Mashatan, Toronto Metropolitan University 
Rei Safavi-Naini, University of Calgary  
Jun Yan, Concordia University 
Nur Zincir-Heywood, Dalhousie University
Areas of Research Modelling and Design Technologies, Monitoring Technologies, Cybersecurity
Non-academic partners Cyber Patterns Inc, Solana Networks, Sunphinx Inc, Waterfall Security Solutions, Calgary Housing Company

Publications:

F. Tazi et al., “A Multi-Dimensional Analysis of IoT Companion Apps: a Look at Privacy, Security and Accessibility,” IEEE Trans. Serv. Comput., pp. 1–14, 2025, doi: 10.1109/TSC.2025.3625817.

Accepted publications in national and international conferences:

A. S. A. Yelgundhalli et al., “SCORED ’25: Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses,” in Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security, Taipei Taiwan: ACM, Nov. 2025, pp. 4898–4899. doi: 10.1145/3719027.3767662.

Y. Zeng et al., “Algorithmic Collusion among EV Charging Stations with Independent Reinforcement Learning Agents,” in 2025 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), North York, ON, Canada: IEEE, Sept. 2025, pp. 1–7. doi: 10.1109/SmartGridComm65349.2025.11204603.

Y. Yuan et al., “Analyzing Agent Collisions in AI-Aided Energy Management Systems,” in 2025 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), North York, ON, Canada: IEEE, Sept. 2025, pp. 1–7. doi: 10.1109/SmartGridComm65349.2025.11204591.

D. Hitaj, G. Pagnotta, F. De Gaspari, L. De Carli, and L. V. Mancini, “Minerva: A File-Based Ransomware Detector,” in Proceedings of the 20th ACM Asia Conference on Computer and Communications Security, Hanoi Vietnam: ACM, Aug. 2025, pp. 576–590. doi: 10.1145/3708821.3733867.

S. Owolabi, F. Rosati, A. Abdellatif, and L. D. Carli, “Characterizing Packages for Vulnerability Prediction,” in 2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR), Ottawa, ON, Canada: IEEE, Apr. 2025, pp. 359–363. doi: 10.1109/MSR66628.2025.00066.

Book chapters: 

S. Pordanesh, S. Bukhari, B. Tan, and L. De Carli, “Hiding in Plain Sight: On the Robustness of AI-Generated Code Detection,” in Detection of Intrusions and Malware, and Vulnerability Assessment, vol. 15748, M. Egele, V. Moonsamy, D. Gruss, and M. Carminati, Eds., in Lecture Notes in Computer Science, vol. 15748. , Cham: Springer Nature Switzerland, 2025, pp. 44–64. doi: 10.1007/978-3-031-97623-0_3.

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