Neural networks are trained as timing models of programs and analyzed via MILP to detect and quantify timing side-channel information leaks.
nature 521(7553), 436 (2015)
2 Pith papers cite this work. Polarity classification is still indexing.
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2019 2representative citing papers
A DL model pre-trained on the Human Connectome Project dataset achieves 67.51% accuracy decoding cognitive states from a new fMRI task after fine-tuning on data from only three subjects.
citing papers explorer
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Efficient Detection and Quantification of Timing Leaks with Neural Networks
Neural networks are trained as timing models of programs and analyzed via MILP to detect and quantify timing side-channel information leaks.
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Deep Transfer Learning For Whole-Brain fMRI Analyses
A DL model pre-trained on the Human Connectome Project dataset achieves 67.51% accuracy decoding cognitive states from a new fMRI task after fine-tuning on data from only three subjects.