SPARK: Static Program Analysis Reasoning and Retrieving Knowledge

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Bibliographische Detailangaben
Veröffentlicht in:	arXiv.org (Nov 3, 2017), p. n/a
1. Verfasser:	Sodsong, Wasuwee
Weitere Verfasser:	Scholz, Bernhard, Chawla, Sanjay
Veröffentlicht:	Cornell University Library, arXiv.org
Schlagworte:	Artificial intelligence Pipelines Case studies Recurrent neural networks Program verification (computers) Compilers Security Machine learning
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Beschreibung
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.
ISSN:	2331-8422
Quelle:	Engineering Database