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We propose an improved abstract interpretation based method for quantifying cache side-channel leakage by addressing two key components of precision loss in existing set-based cache abstractions. Our method targets two key sources of imprecision: (1) imprecision in the abstract transfer function used to update the abstract cache state when interpreting a memory access and (2) imprecision due to the incompleteness of the set-based domain. At the center of our method are two key improvements: (1) the introduction of a new transfer function for updating the abstract cache state which carefully leverages information in the abstract state to prevent the spurious aging of memory blocks and (2) a refinement of the set-based domain based on the finite powerset construction. We show that both the new abstract transformer and the domain refinement enjoy certain enhanced precision properties. We have implemented the method and compared it against the state-of-the-art technique on a suite of benchmark programs implementing both sorting algorithms and cryptographic algorithms. The experimental results show that our method is effective in improving the precision of cache side-channel leakage quantification. 

Deep neural networks (DNNs) are becoming increasingly important components of software, and are considered the state-of-the-art solution for a number of problems, such as image recognition. However, DNNs are far from infallible, and incorrect behavior of DNNs can have disastrous real-world consequences. This paper addresses the problem of architecture-preserving V-polytope provable repair of DNNs. A V-polytope defines a convex bounded polytope using its vertex representation. V-polytope provable repair guarantees that the repaired DNN satisfies the given specification on the infinite set of points in the given V-polytope. An architecture-preserving repair only modifies the parameters of the DNN, without modifying its architecture. The repair has the flexibility to modify multiple layers of the DNN, and runs in polynomial time. It supports DNNs with activation functions that have some linear pieces, as well as fully-connected, convolutional, pooling and residual layers. To the best our knowledge, this is the first provable repair approach that has all of these features. We implement our approach in a tool called APRNN. Using MNIST, ImageNet, and ACAS Xu DNNs, we show that it has better efficiency, scalability, and generalization compared to PRDNN and REASSURE, prior provable repair methods that are not architecture preserving.