AI for security. Security for AI.

Campuses

Research

Binary code representation

Learning representations that connect the compiled forms of a function to what it does, so analysts can find known code across processors, compilers and optimization settings.

Explainable vulnerability analysis

Moving vulnerability assessment from a score to code-level evidence an analyst can inspect, through learned detection, rule generation and patch screening.

Malware beyond known families

Comparing unfamiliar malware with the available evidence instead of forcing it into a known class, using open-set recognition, functional decomposition and authorship analysis.

Firmware and supply-chain security

Recovering the software components and capabilities of a firmware image from the image itself, without relying on the supplier.

Avionics intrusion detection

Resource-aware, adaptive and unsupervised detection for aircraft data buses such as MIL-STD-1553, built on attack analysis and controlled simulation.

Some of our lab members

• 
  Steven H. H. Ding

  I am an Assistant Professor in the School of Information Studies at McGill University and an Adjunct Assistant Professor in the School of Computing at Queen's University. My research applies machine learning to security problems, including binary code analysis, vulnerability analysis, malware and firmware security. I received my PhD from McGill University in 2019.

Publications