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Found 5 papers, 0 papers with code
Efficient Detection and Quantification of Timing Leaks with Neural Networks
As demonstrated in our experiments, both of these tasks are feasible in practice --- making the approach a significant improvement over the state-of-the-art side channel detectors and quantifiers.
Quantitative Mitigation of Timing Side Channels
In contrast to the existing mitigation approaches, we show that in the functional-observation threat model, SCHMIT is scalable and able to maximize confidentiality under the performance overhead bound.
Data-Driven Debugging for Functional Side Channels
On the realistic programs, we show the scalability of FUCHSIA in analyzing functional side channels in Java programs with thousands of methods.
Differential Performance Debugging with Discriminant Regression Trees
We propose a data-driven technique based on discriminant regression tree (DRT) learning problem where the goal is to discriminate among different classes of inputs.
Discriminating Traces with Time
What properties about the internals of a program explain the possible differences in its overall running time for different inputs?
