Transparent and Trustworthy Deep Feature Learning for Cyber-Physical System Security

The latest artificial intelligence (AI) technologies have effectively leveraged the wealth of data from cyber-physical systems (CPSs) to automate intelligent decisions. However, for safety-critical CPS like smart grids and smart cities, the conversion of massive data into actionable information by the AI must be not only effective but also reliable. To this end, this project will develop innovative feature learning methods that can distill raw spatiotemporal data, integrate with establish expert knowledge and system models, and present decision-supporting information with transparency and trustworthiness. With a focus on security monitoring applications in the safety-critical CPS, new scientific tools and practice guides developed by the project will benefit the research and development of AI-based & 5G-enabled CPS products and solutions for Ericsson while enhancing the smart infrastructure security for the general public of Canada.

Faculty Supervisor:

Jun Yan

Student:

Yongxuan Zhang

Partner:

Ericsson Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Concordia University