SPARK: Static Program Analysis Reasoning and Retrieving Knowledge
Sparad:
| I publikationen: | arXiv.org (Nov 3, 2017), p. n/a |
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| Huvudupphov: | |
| Övriga upphov: | , |
| Utgiven: |
Cornell University Library, arXiv.org
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| Ämnen: | |
| Länkar: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2076929544 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2076929544 | ||
| 045 | 0 | |b d20171103 | |
| 100 | 1 | |a Sodsong, Wasuwee | |
| 245 | 1 | |a SPARK: Static Program Analysis Reasoning and Retrieving Knowledge | |
| 260 | |b Cornell University Library, arXiv.org |c Nov 3, 2017 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Artificial intelligence | ||
| 653 | |a Pipelines | ||
| 653 | |a Case studies | ||
| 653 | |a Recurrent neural networks | ||
| 653 | |a Program verification (computers) | ||
| 653 | |a Compilers | ||
| 653 | |a Security | ||
| 653 | |a Machine learning | ||
| 700 | 1 | |a Scholz, Bernhard | |
| 700 | 1 | |a Chawla, Sanjay | |
| 773 | 0 | |t arXiv.org |g (Nov 3, 2017), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2076929544/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/1711.01024 |