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Improving Linux Kernel Fuzzing.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Improving Linux Kernel Fuzzing./
作者:
Oswal, Palash B.
面頁冊數:
1 online resource (43 pages)
附註:
Source: Masters Abstracts International, Volume: 84-11.
Contained By:
Masters Abstracts International84-11.
標題:
Computer science. -
電子資源:
click for full text (PQDT)
ISBN:
9798379515645
Improving Linux Kernel Fuzzing.
Oswal, Palash B.
Improving Linux Kernel Fuzzing.
- 1 online resource (43 pages)
Source: Masters Abstracts International, Volume: 84-11.
Thesis (M.S.)--Carnegie Mellon University, 2023.
Includes bibliographical references
In the Linux Kernel project, one of the most rapidly evolving code bases, fuzz testing is a successful approach for vulnerability detection. However, with the high rate of change in the kernel code, testing each change thoroughly becomes a challenge. With this study, we explore various ways to improve the current Linux Kernel testing landscape. We identify and contribute novel ways of leveraging previously discovered crashes in the Linux Kernel. We call it enriched corpus. We also investigate aspects of program generation for system call fuzzers using iterative deepening. We work with the state of the art kernel fuzzers like syzkaller and HEALER . During this research, we identified many new kernel bugs, and contribute a new open source framework for enriching fuzzer corpus. We also identify challenges in working with corpus and discuss our ongoing experiments and lay out future areas for research. These findings provide insight into improving the Linux Kernel fuzz testing process for higher system reliability and security.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9798379515645Subjects--Topical Terms:
573171
Computer science.
Subjects--Index Terms:
Enriched corpusIndex Terms--Genre/Form:
554714
Electronic books.
Improving Linux Kernel Fuzzing.
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In the Linux Kernel project, one of the most rapidly evolving code bases, fuzz testing is a successful approach for vulnerability detection. However, with the high rate of change in the kernel code, testing each change thoroughly becomes a challenge. With this study, we explore various ways to improve the current Linux Kernel testing landscape. We identify and contribute novel ways of leveraging previously discovered crashes in the Linux Kernel. We call it enriched corpus. We also investigate aspects of program generation for system call fuzzers using iterative deepening. We work with the state of the art kernel fuzzers like syzkaller and HEALER . During this research, we identified many new kernel bugs, and contribute a new open source framework for enriching fuzzer corpus. We also identify challenges in working with corpus and discuss our ongoing experiments and lay out future areas for research. These findings provide insight into improving the Linux Kernel fuzz testing process for higher system reliability and security.
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click for full text (PQDT)
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