SPARK: Static Program Analysis Reasoning and Retrieving Knowledge
Abstract
Program analysis is a technique to reason about programs without executing them, and it has various applications in compilers, integrated development environments, and security. In this work, we present a machine learning pipeline that induces a security analyzer for programs by example. The security analyzer determines whether a program is either secure or insecure based on symbolic rules that were deduced by our machine learning pipeline. The machine pipeline is two-staged consisting of a Recurrent Neural Networks (RNN) and an Extractor that converts an RNN to symbolic rules. To evaluate the quality of the learned symbolic rules, we propose a sampling-based similarity measurement between two infinite regular languages. We conduct a case study using real-world data. In this work, we discuss the limitations of existing techniques and possible improvements in the future. The results show that with sufficient training data and a fair distribution of program paths it is feasible to deducing symbolic security rules for the OpenJDK library with millions lines of code.
Cite
@article{arxiv.1711.01024,
title = {SPARK: Static Program Analysis Reasoning and Retrieving Knowledge},
author = {Wasuwee Sodsong and Bernhard Scholz and Sanjay Chawla},
journal= {arXiv preprint arXiv:1711.01024},
year = {2017}
}