Discovering Software Vulnerabilities Using Data-flow Analysis and Machine Learning

Jorrit Kronjee; Arjen Hommersom; Harald Vranken

doi:10.1145/3230833.3230856

Discovering Software Vulnerabilities Using Data-flow Analysis and Machine Learning

Jorrit Kronjee, Arjen Hommersom, Harald Vranken

Research Line Resilience (part of LIRS program)

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

We present a novel method for static analysis in which we combine data-flow analysis with machine learning to detect SQL injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities in PHP applications. We assembled a dataset from the National Vulnerability Database and the SAMATE project, containing vulnerable PHP code samples and their patched versions in which the vulnerability is solved. We extracted features from the code samples by applying data-flow analysis techniques, including reaching definitions analysis, taint analysis, and reaching constants analysis. We used these features in machine learning to train various probabilistic classifiers. To demonstrate the effectiveness of our approach, we built a tool called WIRECAML, and compared our tool to other tools for vulnerability detection in PHP code. Our tool performed best for detecting both SQLi and XSS vulnerabilities. We also tried our approach on a number of open-source software applications, and found a previously unknown vulnerability in a photo-sharing web application.

Original language	English
Title of host publication	Proceedings of the 13th International Conference on Availability, Reliability and Security
Place of Publication	New York, NY, USA
Publisher	acm
Number of pages	10
ISBN (Print)	978-1-4503-6448-5
DOIs	https://doi.org/10.1145/3230833.3230856
Publication status	Published - 2018
Event	13th International Conference on Availability, Reliability and Security - Hamburg, Germany Duration: 27 Aug 2018 → 30 Aug 2018 https://dl.acm.org/citation.cfm?doid=3230833.3230856

Conference

Conference	13th International Conference on Availability, Reliability and Security
Abbreviated title	ARES 2018
Country	Germany
City	Hamburg
Period	27/08/18 → 30/08/18
Internet address	https://dl.acm.org/citation.cfm?doid=3230833.3230856

Keywords

Software security, data-flow analysis, machine learning, static code analysis, vulnerability detection

Access to Document

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Kronjee, J., Hommersom, A., & Vranken, H. (2018). Discovering Software Vulnerabilities Using Data-flow Analysis and Machine Learning. In Proceedings of the 13th International Conference on Availability, Reliability and Security [6] acm. https://doi.org/10.1145/3230833.3230856

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title = "Discovering Software Vulnerabilities Using Data-flow Analysis and Machine Learning",

abstract = "We present a novel method for static analysis in which we combine data-flow analysis with machine learning to detect SQL injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities in PHP applications. We assembled a dataset from the National Vulnerability Database and the SAMATE project, containing vulnerable PHP code samples and their patched versions in which the vulnerability is solved. We extracted features from the code samples by applying data-flow analysis techniques, including reaching definitions analysis, taint analysis, and reaching constants analysis. We used these features in machine learning to train various probabilistic classifiers. To demonstrate the effectiveness of our approach, we built a tool called WIRECAML, and compared our tool to other tools for vulnerability detection in PHP code. Our tool performed best for detecting both SQLi and XSS vulnerabilities. We also tried our approach on a number of open-source software applications, and found a previously unknown vulnerability in a photo-sharing web application.",

keywords = "Software security, data-flow analysis, machine learning, static code analysis, vulnerability detection",

author = "Jorrit Kronjee and Arjen Hommersom and Harald Vranken",

year = "2018",

doi = "10.1145/3230833.3230856",

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booktitle = "Proceedings of the 13th International Conference on Availability, Reliability and Security",

publisher = "acm",

note = "13th International Conference on Availability, Reliability and Security, ARES 2018 ; Conference date: 27-08-2018 Through 30-08-2018",

url = "https://dl.acm.org/citation.cfm?doid=3230833.3230856",

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Kronjee, J, Hommersom, A & Vranken, H 2018, Discovering Software Vulnerabilities Using Data-flow Analysis and Machine Learning. in Proceedings of the 13th International Conference on Availability, Reliability and Security., 6, acm, New York, NY, USA, 13th International Conference on Availability, Reliability and Security, Hamburg, Germany, 27/08/18. https://doi.org/10.1145/3230833.3230856

Discovering Software Vulnerabilities Using Data-flow Analysis and Machine Learning. / Kronjee, Jorrit; Hommersom, Arjen ; Vranken, Harald.

Proceedings of the 13th International Conference on Availability, Reliability and Security. New York, NY, USA : acm, 2018. 6.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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T1 - Discovering Software Vulnerabilities Using Data-flow Analysis and Machine Learning

AU - Kronjee, Jorrit

AU - Hommersom, Arjen

AU - Vranken, Harald

PY - 2018

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N2 - We present a novel method for static analysis in which we combine data-flow analysis with machine learning to detect SQL injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities in PHP applications. We assembled a dataset from the National Vulnerability Database and the SAMATE project, containing vulnerable PHP code samples and their patched versions in which the vulnerability is solved. We extracted features from the code samples by applying data-flow analysis techniques, including reaching definitions analysis, taint analysis, and reaching constants analysis. We used these features in machine learning to train various probabilistic classifiers. To demonstrate the effectiveness of our approach, we built a tool called WIRECAML, and compared our tool to other tools for vulnerability detection in PHP code. Our tool performed best for detecting both SQLi and XSS vulnerabilities. We also tried our approach on a number of open-source software applications, and found a previously unknown vulnerability in a photo-sharing web application.

AB - We present a novel method for static analysis in which we combine data-flow analysis with machine learning to detect SQL injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities in PHP applications. We assembled a dataset from the National Vulnerability Database and the SAMATE project, containing vulnerable PHP code samples and their patched versions in which the vulnerability is solved. We extracted features from the code samples by applying data-flow analysis techniques, including reaching definitions analysis, taint analysis, and reaching constants analysis. We used these features in machine learning to train various probabilistic classifiers. To demonstrate the effectiveness of our approach, we built a tool called WIRECAML, and compared our tool to other tools for vulnerability detection in PHP code. Our tool performed best for detecting both SQLi and XSS vulnerabilities. We also tried our approach on a number of open-source software applications, and found a previously unknown vulnerability in a photo-sharing web application.

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