SPARK: Static Program Analysis Reasoning and Retrieving Knowledge
Gorde:
| Argitaratua izan da: | arXiv.org (Nov 3, 2017), p. n/a |
|---|---|
| Egile nagusia: | |
| Beste egile batzuk: | , |
| Argitaratua: |
Cornell University Library, arXiv.org
|
| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full text outside of ProQuest |
| Etiketak: |
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
|
| Laburpena: | 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 |
| Baliabidea: | Engineering Database |