SecAppDev 2023 - Machine learning security
Machine learning security
OWASP top 10
Supply chain security
Security engineering for machine learning
Keynote lecture by Gary McGraw in room Lemaire
Monday June 12th, 09:15 - 10:30
How can the adoption of machine learning introduce systematic risk into our applications? This session discusses the results of applying architectural risk analysis to identify the top risks in engineering ML systems.
Key takeaway: The results of an architectural risk analysis (sometimes called a threat model) of ML systems, including the top five (of 78 known) ML security risks
Attacks against machine learning pipelines
Introductory lecture by Davy Preuveneers in room West Wing
Wednesday June 14th, 09:00 - 10:30
This session will explore various attacks against machine learning pipelines and their life cycle, present countermeasures and discuss best practices to make your ML models more robust in adversarial settings.
Key takeaway: ML adds value to applications but also increases the attack surface, imposing a holistic approach to secure the ML pipeline and lifecycle