An Ontario Tech University research project has received funding from the National Cybersecurity Consortium to address the need for cybersecurity in electric vehicle charging stations.
Ontario Tech University’s cybersecurity research expertise in spotlight after National Cybersecurity Consortium (CNC) announces funding of nearly $560,000 over four years for two new research projects at the university.
Ontario Tech projects supported through this program highlight the university’s important research conducted through the Institute for Cybersecurity and Resilient Systemsa multidisciplinary global center of the university focused on cybersecurity research, innovation, teaching and outreach. By focusing on real-world applications, Ontario Tech not only advances cybersecurity research, but also contributes to safer and more trusted digital experiences for individuals and organizations.
The funded initiatives highlight the university’s role as a leader in promoting technological innovation that aligns with global cybersecurity needs, ensuring resilience and proactive measures against emerging threats. The two Ontario Tech projects are among 37 projects across Canada receiving a total of $22.8 million in funding this year through the NCC program. Cybersecurity Innovation Network (CSIN) Program.
Funded projects:
Improving the cybersecurity of electric vehicle charging infrastructure with autonomous and sustainable AI
Funding committed by CNC: $175,294.10
Principal Investigator: Dr. Li Yang, Assistant Professor, Faculty of Business and Information Technology (FBIT), Ontario Tech
Collaborators: Dr. Khalil El-Khatib, professor, FBIT, Ontario Tech; Katarina Grolinger, Canada Research Chair in Technical Applications of Machine Learning, Associate Professor, Department of Electrical and Computer Engineering, Western University; and Automotive Center of Excellence (ACE), Ontario Tech.
This project addresses the critical need for robust cybersecurity in electric vehicle (EV) charging stations, which are an integral part of energy and transportation networks. As the adoption of electric vehicles grows, traditional cybersecurity mechanisms, such as intrusion detection systems (IDS), struggle to counter evolving cyber threats due to limited adaptability, high energy demands and centralized architectures. The project will revolutionize the security of electric vehicle charging stations by developing autonomous, optimized, sustainable and privacy-friendly IDS using advanced artificial intelligence (AI) technologies, including AutoML, TinyML and federated learning. These innovations will enable real-time, energy-efficient and decentralized threat detection, ensuring grid stability, protecting consumer data and supporting the expansion of clean energy solutions. By improving the safety of electric vehicle charging stations, the research builds public confidence in electric vehicle infrastructure, facilitates the development of smart cities, and supports Canada’s transition to electric mobility, resulting in significant economic, social and environmental benefits.
Transformative Adversaries: Leveraging Pre-Trained Generative Transformers for Developing Next-Generation Metamorphic Malware Engines
Funding committed by CNC: $382,352.94
Principal Investigator: Dr. Pooria Madani, Assistant Professor, FBIT, Ontario Tech
Collaborators: Dr. Jeremy Bradbury, Professor, Faculty of Science; Dr. Khalil El-Khatib, Professor, FBIT, Ontario Tech; and Natalija Vlajic, Associate Professor, Lassonde School of Engineering, York University.
This project will investigate how Generative Pre-Trained Transformers (GPT) models can be used offensively and defensively to improve future cyber defenses against the sophisticated and evolving threat of malicious code mutation. Metamorphic malware is a type of malware that rewrites or changes the structure of its code every time it is executed or infected on the system. The ability of metamorphic malware to preserve its malicious functionality while modifying its structure makes it both stealthy and highly lethal, posing a significant challenge as it evades traditional detection methods. Recent advances in AI, particularly GPT models (AI algorithms that teach computers to process information and create new content and ideas like a human brain would) have demonstrated remarkable potential in automation code mutation processes, raising concerns about their misuse to create more adaptive and evasive systems. malware. This research will help us better understand these capabilities and develop strategies to mitigate risks, thereby contributing to stronger and more adaptive cybersecurity defenses.
About the National Cybersecurity Consortium
The National Cybersecurity Consortium is a pan-Canadian network that supports the advancement of the Canadian cybersecurity ecosystem through research and development, commercialization and training by fostering collaboration between universities; private industry; non-profit organizations; provincial, territorial and municipal governments; and other key cybersecurity players.
Funded by Innovation, Science and Economic Development Canada and supervised by the NCC, the Cybersecurity Innovation Network program supports research and development projects, the commercialization of new technologies as well as training, development and retraining efforts carried out by Canadian organizations from the industrial, academic and non-industrial sectors. -profit sectors.
Quotes
“The use of artificial intelligence (AI) in the development of advanced malware is a striking example of how AI can be weaponized. To ensure our future security, we must act today.
– Dr. Pooria MadaniAssistant Professor, Faculty of Business and Information Technology, Ontario Tech University
“Our work to improve the cybersecurity of electric vehicle charging stations is not just about protecting infrastructure; it’s also about building trust and encouraging the adoption of electric transportation, supporting smart cities and contributing to Canada’s clean energy future.
– Dr. Li YangAssistant Professor, Faculty of Business and Information Technology, Ontario Tech University
