DepthStAr: Deep Strange Arguments Detection: Deep Strange Arguments Detection

Michael Berlin; Oded Margalit; Gera Weiss

doi:https://doi.org/10.1007/978-3-030-78086-9_5

DepthStAr: Deep Strange Arguments Detection: Deep Strange Arguments Detection

Michael Berlin, Oded Margalit, Gera Weiss

Department of Computer Science

Research output: Contribution to conference › Paper › 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
Pages	71-85
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-030-78086-9_5
State	Published - 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

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
Computer Science (all)

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https://doi.org/10.1007/978-3-030-78086-9_5

Cite this

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title = "DepthStAr: Deep Strange Arguments Detection: Deep Strange Arguments Detection",

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.",

author = "Michael Berlin and Oded Margalit and Gera Weiss",

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doi = "https://doi.org/10.1007/978-3-030-78086-9_5",

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T2 - 5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021

AU - Berlin, Michael

AU - Margalit, Oded

AU - Weiss, Gera

PY - 2021

Y1 - 2021

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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Y2 - 8 July 2021 through 9 July 2021

ER -

DepthStAr: Deep Strange Arguments Detection: Deep Strange Arguments Detection

Abstract

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