DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Cha, Sang Kil | - |
dc.contributor.advisor | 차상길 | - |
dc.contributor.author | Choi, Jaeseung | - |
dc.date.accessioned | 2023-06-23T19:34:48Z | - |
dc.date.available | 2023-06-23T19:34:48Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=996450&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/309293 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 정보보호대학원, 2022.2,[v, 70 p. :] | - |
dc.description.abstract | Program-aware fuzzing is a way to utilize knowledge about program behaviors during a fuzzing campaign. In this dissertation, we argue that we can extend the capacity of program-aware fuzzing by applying binary-level static analysis on previously less-explored targets. First, OS-level system binaries are significantly larger than regular application binaries, and they could hardly become a target for static analysis. We enable a scalable static analysis on system binaries with the help of modular analysis and demonstrate the first Windows kernel fuzzer that is aware of high-level system call semantics. Second, there are recently emerging execution environments, such as EVM, where traditional binary-level analysis does not apply. We broaden the scope of static binary analysis to the EVM architecture and achieve the program-awareness in smart contract fuzzing by inferring meaningful function call orders with data-flow knowledge. With the two systems, we demonstrate that the program knowledge obtained from our static analyses can indeed enhance the performance of fuzzing. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.title | Extending the capacity of program-aware fuzzing with binary-level static analysis | - |
dc.title.alternative | 바이너리 정적 분석을 활용한 프로그램 인식형 퍼징 기술의 역량 확장 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :정보보호대학원, | - |
dc.contributor.alternativeauthor | 최재승 | - |
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