Security Engineering for Machine Learning - Dr. Gary McGraw - Wash/NoVA Computer Society Chapter

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#Machine Learning #Security #Artificial Intelligence #Taxonomy #Raw Data #Training #Dataset #Learning Algorithm #Inference Algorithm #Transfer Learning

Dr. McGraw methodically describes a taxonomy of known attacks on the architecture of an ML system. Developed at the Berryville Institute of Machine Learning, it highlights many of the technical and social risks of this increasingly important field.

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 two threads: building a taxonomy of known attacks on ML and 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.

Dr. McGraw methodically describes a taxonomy of known attacks on the architecture of an ML system. Developed at the Berryville Institute of Machine Learning, it highlights many of the technical and social risks of this increasingly important field.

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