Berryville Institute of Machine Learning

Berryville Institute of Machine Learning
berryvilleiml.com

Our research at BIML focuses on three threads: building a taxonomy of known attacks on ML, exploring a hypothesis of representation and ML risk, and performing an architectural risk analysis (sometimes called a threat model) of ML systems in general.

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BIML is concerned by the systematic risk invoked by adopting ML in a haphazard fashion. Addressing security risk in ML is not a new idea, but most previous work has focused on either particular attacks against running ML systems (a kind of dynamic analysis) or on operational security issues surrounding ML. While we encourage these lines of inquiry, our research is focused on understanding and categorizing security engineering risks introduced by ML at the design level.