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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蕭旭君(Hsu-Chun Hsiao) | |
| dc.contributor.author | Chien-Yuan Wang | en |
| dc.contributor.author | 王建元 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:21:09Z | - |
| dc.date.available | 2021-08-06 | |
| dc.date.available | 2022-11-23T09:21:09Z | - |
| dc.date.copyright | 2021-08-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-26 | |
| dc.identifier.citation | [1] Llvm. https://llvm.org/, 2003. [2] fuzzertestsuite. https://github.com/google/fuzzer-test-suite, 2017. [3] libFuzzer–a library for coverageguided fuzz testing. https://llvm.org/docs/ LibFuzzer.html, 2017. [4] Sanitizercoverage in llvm. SanitizerCoverage.html, 2017. https://clang.llvm.org/docs/ [5] Fuzzbench. https://github.com/google/fuzzbench, 2020. [6] R. Agrawal. Sample mean based index policies with o(log n) regret for the multiarmed bandit problem. Advances in Applied Probability, 27(4):1054–1078, 1995. [7] D. Arthur and S. Vassilvitskii. Kmeans++: The advantages of careful seeding. In Proceedings of the Eighteenth Annual ACMSIAM Symposium on Discrete Algorithms, SODA ’07, page 1027–1035, USA, 2007. Society for Industrial and Applied Mathematics. [8] M. Böhme, V. J. M. Manès, and S. K. Cha. Boosting fuzzer efficiency: An information theoretic perspective. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020, page 678– 689, New York, NY, USA, 2020. Association for Computing Machinery. [9] M. Böhme, V.T. Pham, and A. Roychoudhury. Coveragebased greybox fuzzing as markov chain. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS ’16, page 1032–1043, New York, NY, USA, 2016. Association for Computing Machinery. [10] Y. Chen, Y. Jiang, F. Ma, J. Liang, M. Wang, C. Zhou, X. Jiao, and Z. Su. Enfuzz: Ensemble fuzzing with seed synchronization among diverse fuzzers. In 28th USENIX Security Symposium (USENIX Security 19), pages 1967–1983, Santa Clara, CA, Aug. 2019. USENIX Association. [11] A. Fioraldi, D. Maier, H. Eißfeldt, and M. Heuse. Afl++ : Combining incremental steps of fuzzing research. In 14th USENIX Workshop on Offensive Technologies (WOOT 20). USENIX Association, Aug. 2020. [12] Google. Ossfuzz continuous fuzzing of open source software. https://github.com/google/oss-fuzz, 2016. [13] E. Güler, P. Görz, E. Geretto, A. Jemmett, S. Österlund, H. Bos, C. Giuffrida, and T. Holz. Cupid : Automatic fuzzer selection for collaborative fuzzing. In Annual Computer Security Applications Conference, ACSAC ’20, page 360–372, New York, NY, USA, 2020. Association for Computing Machinery. [14] A. Helin. Radamsa. https://gitlab.com/akihe/radamsa, 2016. [15] C. Lemieux and K. Sen. Fairfuzz: A targeted mutation strategy for increasing greybox fuzz testing coverage. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE 2018, page 475–485, New York, NY, USA, 2018. Association for Computing Machinery. [16] J. Liang, Y. Jiang, Y. Chen, M. Wang, C. Zhou, and J. Sun. Pafl: Extend fuzzing optimizations of single mode to industrial parallel mode. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2018, page 809814, New York, NY, USA, 2018. Association for Computing Machinery. [17] J. MacQueen. Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Symp. Math. Stat. Probab., Univ. Calif. 1965/66, 1, 281297 (1967)., 1967. [18] S. Rawat, V. Jain, A. Kumar, L. Cojocar, C. Giuffrida, and H. Bos. VUzzer : Application aware Evolutionary Fuzzing. NDSS’17. 2017. [19] N. Sarten. Dkm. https://github.com/genbattle/dkm, 2015. [20] J. Wang, C. Song, and H. Yin. Reinforcement learningbased hierarchical seed scheduling for greybox fuzzing. Proceedings 2021 Network and Distributed System Security Symposium, 2021. [21] Wikipedia contributors. kmeans++ — Wikipedia, the free encyclopedia. https://en.wikipedia.org/wiki/K-means%2B%2B, 2021. [Online; accessed 3June2021]. [22] Wikipedia contributors. kmeans clustering — Wikipedia, the free encyclopedia. https://en.wikipedia.org/wiki/K-means_clustering, 2021. [Online; accessed 3June2021]. [23] J. Ye, B. Zhang, R. Li, C. Feng, and C. Tang. Program state sensitive parallel fuzzing for real world software. IEEE Access, 7:42557–42564, 2019. [24] I. Yun, S. Lee, M. Xu, Y. Jang, and T. Kim. QSYM : A practical concolic execution engine tailored for hybrid fuzzing. In 27th USENIX Security Symposium (USENIX Security 18), pages 745–761, Baltimore, MD, Aug. 2018. USENIX Association. [25] I. Yun, S. Lee, M. Xu, Y. Jang, and T. Kim. QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing. In Proceedings of the 27th USENIX Security Symposium (Security), Baltimore, MD, Aug. 2018. [26] M. Zalewski. american fuzzy lop. http://lcamtuf.coredump.cx/afl/, 2015. [27] S. Österlund, E. Geretto, A. Jemmett, E. Güler, P. Görz, T. Holz, C. Giuffrida, and H. Bos. CollabFuzz: A Framework for Collaborative Fuzzing. In EuroSec, Apr. 2021. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80015 | - |
| dc.description.abstract | 模糊測試是一個被廣泛使用來自動化偵測軟體漏洞的技術。過去的模糊器使用許多的模糊測試技巧並且在不同的面向表現優異。最近的研究顯示讓不同的模糊器一起合作可以使得它們探索到單一模糊器無法達到的路徑，也可以找到更多一般模糊器找不到的軟體漏洞。然而目前的種子同步機制卻並沒有考慮不同模糊 器之間的差異，而是將它們平等的對待。這份研究提出的選擇性種子同步機制就是在處理這個問題，透過使不同模糊器專注在測試不同的種子上來更進一步利用模糊器之間的多樣性。我們基於選擇性種子同步的機制實作了 3S-Fuzz，並且在 Google’s fuzzer-test-suite 上執行 24 小時來進行比較。我們的實驗結果顯示選擇性種子同步的效果比 Enfuzz 好。並且證實合作式平行模糊測試可以透過更細緻的種子同步進行更進一步的優化。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:21:09Z (GMT). No. of bitstreams: 1 U0001-1907202111433400.pdf: 1391531 bytes, checksum: f5eeca00ef5599360c729072a465b1c9 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Acknowledgements 2 摘要 3 Abstract 4 Contents 6 List of Figures 8 List of Tables 9 1 Introduction 1 2 Background and Related Work 5 2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Coverageguided Fuzzing . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Improving Seed Scheduling . . . . . . . . . . . . . . . . . . . . . . 6 2.1.3 Generationbased Fuzzing . . . . . . . . . . . . . . . . . . . . . . 7 2.1.4 Parallel Fuzzing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Clustering Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.2 Multiarmed Bandit Problem . . . . . . . . . . . . . . . . . . . . . 11 2.2.3 Upper Confidence Bound Algorithm . . . . . . . . . . . . . . . . . 12 3 Design 14 3.1 Clustering Seeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.1 Seed Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.1.2 Edge Collision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2 Selecting Seeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.1 Seed Selection as Multiarmed Bandit Problem . . . . . . . . . . . 19 3.3 Selective Seed Synchronization Fuzzing (3SFuzz) . . . . . . . . . . 21 3.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4 Result 25 4.1 Environment and Parameters . . . . . . . . . . . . . . . . . . . . . . 26 4.1.1 Computing Resources . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.1.2 Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.1.3 Coverage Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.1.4 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2 RQ1. Parallel Fuzzing . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.3 RQ2. Effect of Clustering . . . . . . . . . . . . . . . . . . . . . . . 28 4.4 RQ3. Effect of UCB . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.5 RQ4. vs Enfuzz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.6 RQ5. vs AFL++ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5 Conclusion and Future Work 37 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 References 39 | |
| dc.language.iso | en | |
| dc.subject | 種子同步 | zh_TW |
| dc.subject | 模糊測試 | zh_TW |
| dc.subject | Seed Synchronization | en |
| dc.subject | Fuzzing | en |
| dc.title | 3S-Fuzz: 平行模糊測試中選擇性種子同步 | zh_TW |
| dc.title | 3S-Fuzz: Selective Seed Synchronization in Parallel | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃世昆(Hsin-Tsai Liu),黃俊穎(Chih-Yang Tseng) | |
| dc.subject.keyword | 模糊測試,種子同步, | zh_TW |
| dc.subject.keyword | Fuzzing,Seed Synchronization, | en |
| dc.relation.page | 42 | |
| dc.identifier.doi | 10.6342/NTU202101561 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-07-27 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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|---|---|---|---|
| U0001-1907202111433400.pdf | 1.36 MB | Adobe PDF | 檢視/開啟 |
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