Data-Driven Synthesis of Provably Sound Side Channel Analyses

We propose a data-driven method for synthesizing static analyses to detect side-channel information leaks in cryptographic software. Compared to the conventional way of manually crafting such static analyzers, which can be tedious, error prone and suboptimal, our learning-based technique is not only automated but also provably sound. Our analyzer consists of a set of type-inference rules learned from the training data, i.e., example code snippets annotated with the ground truth. Internally, we use syntax-guided synthesis (SyGuS) to generate new recursive features and decision tree learning (DTL) to generate analysis rules based on these features. We guarantee soundness by proving each learned analysis rule via a technique called query containment checking. We have implemented our technique in the LLVM compiler and used it to detect power side channels in C programs that implement cryptographic protocols. Our results show that, in addition to being automated and provably sound during synthesis, our analyzer can achieve the same empirical accuracy as two state-of-the-art, manually-crafted analyzers while being 300X and 900X faster, respectively.