SPARK: Static Program Analysis Reasoning and Retrieving Knowledge

Bewaard in:

Bibliografische gegevens
Gepubliceerd in:	arXiv.org (Nov 3, 2017), p. n/a
Hoofdauteur:	Sodsong, Wasuwee
Andere auteurs:	Scholz, Bernhard, Chawla, Sanjay
Gepubliceerd in:	Cornell University Library, arXiv.org
Onderwerpen:	Artificial intelligence Pipelines Case studies Recurrent neural networks Program verification (computers) Compilers Security Machine learning
Online toegang:	Citation/Abstract Full text outside of ProQuest
Tags:	Voeg label toe Geen labels, Wees de eerste die dit record labelt!

Omschrijving
Samenvatting:	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
Bron:	Engineering Database