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Machine Learning for Black-Box Fuzzing of Network Protocols

  • Rong Fan
  • Yaoyao Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10631)

Abstract

As the network services are gradually complex and important, the security problems of their protocols become more and more serious. Vulnerabilities in network protocol implementations can expose sensitive user data to attackers or execute arbitrary malicious code deployed by attackers. Fuzzing is an effective way to find security vulnerabilities for network protocols. But it is difficult to fuzz network protocols if the specification and implementation code of the protocol are both unavailable. In this paper, we propose a method to automatically generate test cases for black-box fuzzing of proprietary network protocols. Our method uses neural-network-based machine learning techniques to learn a generative input model of proprietary network protocols by processing their traffic, and generating new messages using the learnt model. These new messages can be used as test cases to fuzz the implementations of corresponding protocols.

Keywords

Black-box fuzzing Proprietary network protocol Machine learning 

References

  1. 1.
    Sutton, M., Greene, A., Amini, P.: Fuzzing: Brute Force Vulnerability Discovery. Pearson Education, London (2007)Google Scholar
  2. 2.
    Godefroid, P., Levin, M.Y., Molnar, D.A., et al.: Automated whitebox fuzz testing. In: NDSS, vol. 8, pp. 151–166 (2008)Google Scholar
  3. 3.
    Miller, C., Peterson, Z.N.: Analysis of mutation and generation-based fuzzing. Technical report, Independent Security Evaluators (2007)Google Scholar
  4. 4.
    Sotirov, A.I.: Automatic vulnerability detection using static source code analysis. Ph.D. thesis, University of Alabama (2005)Google Scholar
  5. 5.
    Chess, B., McGraw, G.: Static analysis for security. IEEE Secur. Priv. 2(6), 76–79 (2004)CrossRefGoogle Scholar
  6. 6.
    Godefroid, P., Kiezun, A., Levin, M.Y.: Grammar-based whitebox fuzzing. In: ACM Sigplan Notices, vol. 43, pp. 206–215. ACM (2008)CrossRefGoogle Scholar
  7. 7.
    Cadar, C., Godefroid, P., Khurshid, S., Păsăreanu, C.S., Sen, K., Tillmann, N., Visser, W.: Symbolic execution for software testing in practice: preliminary assessment. In: Proceedings of the 33rd International Conference on Software Engineering, pp. 1066–1071. ACM (2011)Google Scholar
  8. 8.
    Cadar, C., Sen, K.: Symbolic execution for software testing: three decades later. Commun. ACM 56(2), 82–90 (2013)CrossRefGoogle Scholar
  9. 9.
    Schwartz, E.J., Avgerinos, T., Brumley, D.: All you ever wanted to know about dynamic taint analysis and forward symbolic execution (but might have been afraid to ask). In: 2010 IEEE Symposium on Security and Privacy (SP), pp. 317–331. IEEE (2010)Google Scholar
  10. 10.
    Amini, P., Portnoy, A.: Sulley: pure Python fully automated and unattended fuzzing framework (2013)Google Scholar
  11. 11.
    Eddington, M.: Peach fuzzing platform. In: Peach Fuzzer, p. 34 (2011)Google Scholar
  12. 12.
  13. 13.
    Gorbunov, S., Rosenbloom, A.: Autofuzz: automated network protocol fuzzing framework. IJCSNS 10(8), 239 (2010)Google Scholar
  14. 14.
    Gascon, H., Wressnegger, C., Yamaguchi, F., Arp, D., Rieck, K.: Pulsar: stateful black-box fuzzing of proprietary network protocols. In: Thuraisingham, B., Wang, X.F., Yegneswaran, V. (eds.) SecureComm 2015. LNICST, vol. 164, pp. 330–347. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-28865-9_18CrossRefGoogle Scholar
  15. 15.
    Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  16. 16.
    Comparetti, P.M., Wondracek, G., Kruegel, C., Kirda, E.: Prospex: protocol specification extraction. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 110–125. IEEE (2009)Google Scholar
  17. 17.
    Caballero, J., Yin, H., Liang, Z., Song, D.: Polyglot: automatic extraction of protocol message format using dynamic binary analysis. In: Proceedings of the 14th ACM Conference on Computer and Communications Security, pp. 317–329. ACM (2007)Google Scholar
  18. 18.
    Beddoe, M.: The protocol informatics project (2004)Google Scholar
  19. 19.
    Cui, W., Kannan, J., Wang, H.J.: Discoverer: automatic protocol reverse engineering from network traces. In: USENIX Security Symposium, pp. 1–14 (2007)Google Scholar
  20. 20.
    Wang, Y., Li, X., Meng, J., Zhao, Y., Zhang, Z., Guo, L.: Biprominer: automatic mining of binary protocol features. In: 2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pp. 179–184. IEEE (2011)Google Scholar
  21. 21.
    Wang, Y., Yun, X., Shafiq, M.Z., Wang, L., Liu, A.X., Zhang, Z., Yao, D., Zhang, Y., Guo, L.: A semantics aware approach to automated reverse engineering unknown protocols. In: 2012 20th IEEE International Conference on Network Protocols (ICNP), pp. 1–10. IEEE (2012)Google Scholar
  22. 22.
    Luo, J.Z., Yu, S.Z.: Position-based automatic reverse engineering of network protocols. J. Netw. Comput. Appl. 36(3), 1070–1077 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Institute of TechnologyBeijingChina

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