Security Engineering for Machine Learning
Machine Learning appears to have made impressive progress on many tasks including image classification, machine translation, autonomous vehicle control, playing complex games including chess, Go, and Atari video games, and more. This has led to much breathless popular press coverage of Artificial Intelligence, and has elevated deep learning to an almost magical status in the eyes of the public. ML, especially of the deep learning sort, is not magic, however. ML has become so popular that its application, though often poorly understood and partially motivated by hype, is exploding. In my view, this is not necessarily a good thing. I am concerned with the systematic risk invoked by adopting ML in a haphazard fashion. Our research at the Berryville Institute of Machine Learning (BIIML) is focused on understanding and categorizing security engineering risks introduced by ML at the design level. Though the idea of addressing security risk in ML is not a new one, 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. This talk focuses on the results of an architectural risk analysis (sometimes called a threat model) of ML systems in general. A list of the top five (of 78 known) ML security risks will be presented.
I will be describing the work of the Berryville Institute of Machine Learning (BIML) with a focus on architectural security risks built into ML. https://berryvilleiml.com/
See this document for technical detail: https://berryvilleiml.com/results/ara.pdf