Boosting Fuzzer Efficiency: An Information Theoretic Perspective.
This article discusses the concept of fuzzing as a learning process, using Shannon's entropy to quantify the efficiency of a fuzzer in discovering new behaviors of a program. The authors propose an entropy-based power schedule called "Entropic" for greybox fuzzing, assigning more energy to seeds tha...
| Publicado en: | Communications of the ACM Vol. 66; no. 11; pp. 89 - 98 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Association for Computing Machinery
Nov2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=173131753&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 173131753 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Nov2023 vid: 66 iid: 11 pid: 68 pub: Association for Computing Machinery artinfo: ui: 173131753 10.1145/3611019 ppf: 89 ppct: 9 formats: tig: atl: Boosting Fuzzer Efficiency: An Information Theoretic Perspective. aug: au: Böhme, Marcel Manès, Valentin J. M. Sang Kil Cha affil: MPI-SP, Germany; Monash University, Australia CSRC, KAIST, Korea su: Entropy (Information theory) Uncertainty (Information theory) Computer software testing Information theory sug: subj: Entropy (Information theory) Uncertainty (Information theory) Computer software testing Information theory ab: This article discusses the concept of fuzzing as a learning process, using Shannon's entropy to quantify the efficiency of a fuzzer in discovering new behaviors of a program. The authors propose an entropy-based power schedule called "Entropic" for greybox fuzzing, assigning more energy to seeds that reveal more information about a program's behaviors. This approach is implemented in the popular greybox fuzzer LibFuzzer and has been integrated into Google and Microsoft's fuzzing platforms. The paper highlights that the efficiency of a fuzzer is determined by the average information each generated input reveals about a program's behaviors. The authors conducted experiments with over 250 open-source programs, demonstrating a substantial improvement in efficiency and confirming their hypothesis that an efficient fuzzer maximizes information. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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