DepthStAr: Deep Strange Arguments Detection

Michael Berlin; Oded Margalit; Gera Weiss

doi:10.1007/978-3-030-78086-9_5

DepthStAr: Deep Strange Arguments Detection

Michael Berlin, Oded Margalit, Gera Weiss

Department of Computer Science

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

Abstract

We present a tool for detecting a new type of bad smell in software code and describe how it was used to find critical security bugs, some of which exist in Linux code for many years and are still present in current distributions. Our tool applies state-of-the-art formal methods and static analysis techniques to scan the execution paths of programs. In this scan, the tool detects conditions that may lead to calling certain functions with strange combinations of arguments, called Abnormal Argument Case (AAC) in this paper. These conditions are presented to the developers as they often point at potential bugs and security vulnerabilities. The paper explains how the tool works and describes an empirical evaluation of its performance.

Original language	English
Title of host publication	Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings
Editors	Shlomi Dolev, Oded Margalit, Benny Pinkas, Alexander Schwarzmann
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	71-85
Number of pages	15
ISBN (Print)	9783030780852
DOIs	https://doi.org/10.1007/978-3-030-78086-9_5
State	Published - 1 Jul 2021
Event	5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021 - Be'er Sheva, Israel Duration: 8 Jul 2021 → 9 Jul 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12716 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021
Country/Territory	Israel
City	Be'er Sheva
Period	8/07/21 → 9/07/21

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-78086-9_5

Cite this

Berlin, M., Margalit, O., & Weiss, G. (2021). DepthStAr: Deep Strange Arguments Detection. In S. Dolev, O. Margalit, B. Pinkas, & A. Schwarzmann (Eds.), Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings (pp. 71-85). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12716 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-78086-9_5

Berlin, Michael ; Margalit, Oded ; Weiss, Gera. / DepthStAr : Deep Strange Arguments Detection. Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. editor / Shlomi Dolev ; Oded Margalit ; Benny Pinkas ; Alexander Schwarzmann. Springer Science and Business Media Deutschland GmbH, 2021. pp. 71-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We present a tool for detecting a new type of bad smell in software code and describe how it was used to find critical security bugs, some of which exist in Linux code for many years and are still present in current distributions. Our tool applies state-of-the-art formal methods and static analysis techniques to scan the execution paths of programs. In this scan, the tool detects conditions that may lead to calling certain functions with strange combinations of arguments, called Abnormal Argument Case (AAC) in this paper. These conditions are presented to the developers as they often point at potential bugs and security vulnerabilities. The paper explains how the tool works and describes an empirical evaluation of its performance.",

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Berlin, M, Margalit, O & Weiss, G 2021, DepthStAr: Deep Strange Arguments Detection. in S Dolev, O Margalit, B Pinkas & A Schwarzmann (eds), Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12716 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 71-85, 5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021, Be'er Sheva, Israel, 8/07/21. https://doi.org/10.1007/978-3-030-78086-9_5

DepthStAr: Deep Strange Arguments Detection. / Berlin, Michael; Margalit, Oded ; Weiss, Gera.
Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. ed. / Shlomi Dolev; Oded Margalit; Benny Pinkas; Alexander Schwarzmann. Springer Science and Business Media Deutschland GmbH, 2021. p. 71-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12716 LNCS).

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

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N2 - We present a tool for detecting a new type of bad smell in software code and describe how it was used to find critical security bugs, some of which exist in Linux code for many years and are still present in current distributions. Our tool applies state-of-the-art formal methods and static analysis techniques to scan the execution paths of programs. In this scan, the tool detects conditions that may lead to calling certain functions with strange combinations of arguments, called Abnormal Argument Case (AAC) in this paper. These conditions are presented to the developers as they often point at potential bugs and security vulnerabilities. The paper explains how the tool works and describes an empirical evaluation of its performance.

AB - We present a tool for detecting a new type of bad smell in software code and describe how it was used to find critical security bugs, some of which exist in Linux code for many years and are still present in current distributions. Our tool applies state-of-the-art formal methods and static analysis techniques to scan the execution paths of programs. In this scan, the tool detects conditions that may lead to calling certain functions with strange combinations of arguments, called Abnormal Argument Case (AAC) in this paper. These conditions are presented to the developers as they often point at potential bugs and security vulnerabilities. The paper explains how the tool works and describes an empirical evaluation of its performance.

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Berlin M, Margalit O , Weiss G. DepthStAr: Deep Strange Arguments Detection. In Dolev S, Margalit O, Pinkas B, Schwarzmann A, editors, Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 71-85. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-78086-9_5

DepthStAr: Deep Strange Arguments Detection

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