An Iootfuzzer Method for Vulnerability Mining of Internet of Things Devices based on Binary Source Code and Feedback Fuzzing Article Swipe
Guangxin Guo
,
Chao Wang
,
Jiahan Dong
,
Bowen Li
,
Xiaohu Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2022.0130781
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2022.0130781
With the technological progress of the Internet and 5G communication network, more and more Internet of Things devices are used in it. Limited by the cost, power consumption and other factors of Internet of Things devices, the systems carried by the Internet of Things devices often lack the security protection provided by larger equipment systems such as desktop computers. Because the current personal computers and servers mostly use the x86 architecture, and the previous research on security tools or hardware-based security analysis feature support is mostly based on the x86 architecture, the traditional security analysis techniques cannot be applied to the current large-scale ARM-based and MIPS-based Internet of Things devices. Based on this, this paper studies the firmware binary program of common Linux-based Internet of Things devices. A binary static instrumentation technology based on taint information analysis is proposed. The paper also analyzes how to use the binary static instrumentation technology combined with static analysis results to rewrite binary programs and obtain taint path information when binary programs are executed. Firmware binary fuzzing technology based on model constraints and path feedback is studied to cover more dangerous execution paths in the target program. Finally, iootfuzzer, a prototype vulnerability mining system for firmware binaries of Internet of Things devices, is used to test and analyze the two technologies. The results show that its fuzzing efficiency for Internet of Things devices is better than other fuzzing technologies such as boofuzz and Peach 3. It can fill in some gaps in the current security analysis tools for the Internet of Things devices and improve the efficiency of security analysis for Internet of Things devices, which contributes to the field through automated security vulnerability detection systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2022.0130781
- http://thesai.org/Downloads/Volume13No7/Paper_81-An_Iootfuzzer_Method_for_Vulnerability_Mining_of_Internet_of_Things.pdf
- OA Status
- diamond
- Cited By
- 1
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4289865852
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4289865852Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14569/ijacsa.2022.0130781Digital Object Identifier
- Title
-
An Iootfuzzer Method for Vulnerability Mining of Internet of Things Devices based on Binary Source Code and Feedback FuzzingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Guangxin Guo, Chao Wang, Jiahan Dong, Bowen Li, Xiaohu WangList of authors in order
- Landing page
-
https://doi.org/10.14569/ijacsa.2022.0130781Publisher landing page
- PDF URL
-
https://thesai.org/Downloads/Volume13No7/Paper_81-An_Iootfuzzer_Method_for_Vulnerability_Mining_of_Internet_of_Things.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://thesai.org/Downloads/Volume13No7/Paper_81-An_Iootfuzzer_Method_for_Vulnerability_Mining_of_Internet_of_Things.pdfDirect OA link when available
- Concepts
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Fuzz testing, Computer science, Firmware, The Internet, Computer security, Byte, Internet security, Vulnerability (computing), Embedded system, Software, Operating system, Information security, Security serviceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
-
17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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