Advertisement

Comprehensive Analysis and Detection of Flash-Based Malware

  • Christian WressneggerEmail author
  • Fabian Yamaguchi
  • Daniel Arp
  • Konrad Rieck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9721)

Abstract

Adobe Flash is a popular platform for providing dynamic and multimedia content on web pages. Despite being declared dead for years, Flash is still deployed on millions of devices. Unfortunately, the Adobe Flash Player increasingly suffers from vulnerabilities, and attacks using Flash-based malware regularly put users at risk of being remotely attacked. As a remedy, we present Gordon, a method for the comprehensive analysis and detection of Flash-based malware. By analyzing Flash animations at different levels during the interpreter’s loading and execution process, our method is able to spot attacks against the Flash Player as well as malicious functionality embedded in ActionScript code. To achieve this goal, Gordon combines a structural analysis of the container format with guided execution of the contained code, a novel analysis strategy that manipulates the control flow to maximize the coverage of indicative code regions. In an empirical evaluation with 26,600 Flash samples collected over 12 consecutive weeks, Gordon significantly outperforms related approaches when applied to samples shortly after their first occurrence in the wild, demonstrating its ability to provide timely protection for end users.

Keywords

Adobe flash Malware Classification 

Notes

Acknowledgments

The authors would like to thank Emiliano Martinez of VirusTotal for supporting the acquisition of malicious Flash files. Furthermore, we gratefully acknowledge funding from the German Federal Ministry of Education and Research (BMBF) under the projects APT-Sweeper (FKZ 16KIS0307) and INDI (FKZ 16KIS0154K) as well as the German Research Foundation (DFG) under project DEVIL (RI 2469/1-1).

References

  1. 1.
    Adobe Systems Incooperated: ActionScript virtual machine 2 (AVM2) overview. Technical report, Adobe System Incooperated (2007)Google Scholar
  2. 2.
    Adobe Systems Incooperated: SWF file format specification. Technical report, Adobe System Incooperated (2013)Google Scholar
  3. 3.
    Aho, A.V., Sethi, R., Ullman, J.D.: Compilers Principles, Techniques, and Tools, 2nd edn. Addison-Wesley, Reading (2006)zbMATHGoogle Scholar
  4. 4.
    Baecher, P., Koetter, M.: libemu - x86 Shellcode Emulation (2008)Google Scholar
  5. 5.
    Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. In: Proceedings of International Conference on Machine Learning (ICML) (2012)Google Scholar
  6. 6.
    Brumley, D., Hartwig, C., Liang, Z., Newsome, J., Song, D., Yin, H.: Automatically identifying trigger-based behavior in malware. In: Lee, W., Wang, C., Dagon, D. (eds.) Botnet Detection, pp. 65–88. Springer, US (2008)CrossRefGoogle Scholar
  7. 7.
    Canali, D., Cova, M., Vigna, G., Kruegel, C.: Prophiler: a fast filter for the large-scale detection of malicious web pages. In: Proceedings of the International World Wide Web Conference (WWW), pp. 197–206, April 2011Google Scholar
  8. 8.
    Cavallaro, L., Saxena, P., Sekar, R.: On the limits of information flow techniques for malware analysis and containment. In: Zamboni, D. (ed.) DIMVA 2008. LNCS, vol. 5137, pp. 143–163. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Cavnar, W., Trenkle, J.: N-gram-based text categorization. In: Proceedings of SDAIR, Las Vegas, pp. 161–175, NV, USA, April 1994Google Scholar
  10. 10.
    Chen, X., Andersen, J., Mao, Z.M., Bailey, M., Nazario, J.: Towards an understanding of anti-virtualization and anti-debugging behavior in modern malware. In: Proceedings of Conference on Dependable Systems and Networks (DSN), pp. 177–186 (2008)Google Scholar
  11. 11.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  12. 12.
    Cova, M., Felmetsger, V., Banks, G., Vigna, G.: Static detection of vulnerabilities in x86 executables. In: Proceedings of Annual Computer Security Applications Conference (ACSAC), pp. 269–278 (2006)Google Scholar
  13. 13.
    Cova, M., Kruegel, C., Vigna, G.: Detection and analysis of drive-by-download attacks and malicious JavaScript code. In: Proceedings of the International World Wide Web Conference (WWW), pp. 281–290 (2010)Google Scholar
  14. 14.
    Crandall, J.R., Wassermann, G., Oliveira, D.A.S., Su, Z., Wu, S.F., Chong, F.T.: Temporal search: detecting hidden malware timebombs with virtual machines. In: Proceedings of International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 25–36 (2006)Google Scholar
  15. 15.
    Cretu, G., Stavrou, A., Locasto, M., Stolfo, S., Keromytis, A.: Casting out demons: Sanitizing training data for anomaly sensors. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 81–95 (2008)Google Scholar
  16. 16.
    Curtsinger, C., Livshits, B., Zorn, B., Seifert, C.: Zozzle: fast and precise in-browser JavaScript malware detection. In: Proceedings of USENIX Security Symposium, pp. 33–48 (2011)Google Scholar
  17. 17.
    Fogla, P., Lee, W.: Evading network anomaly detection systems: formal reasoning and practical techniques. In: Proceedings of ACM Conference on Computer and Communications Security (CCS), pp. 59–68 (2006)Google Scholar
  18. 18.
    Fogla, P., Sharif, M., Perdisci, R., Kolesnikov, O., Lee, W.: Polymorphic blending attacks. In: Proceedings of USENIX Security Symposium, pp. 241–256 (2006)Google Scholar
  19. 19.
    Ford, S., Cova, M., Kruegel, C., Vigna, G.: Analyzing and detecting malicious flash advertisements. In: Proceedings of Annual Computer Security Applications Conference (ACSAC), pp. 363–372 (2009)Google Scholar
  20. 20.
    gnash. GNU Gnash. https://www.gnu.org/software/gnash. Accessed April 2016
  21. 21.
    Hirvonen, T.: Dynamic flash instrumentation for fun and profit. In: Proceedings of Black Hat USA (2014)Google Scholar
  22. 22.
    httparchive. http://www.httparchive.org. Accessed April 2016
  23. 23.
    Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I.P., Tygar, J.D.: Adversarial machine learning. In: Proceedings of ACM Workshop on Artificial Intelligence and Security (AISEC), pp. 43–58 (2011)Google Scholar
  24. 24.
    Jang, J., Agrawal, A., Brumley, D.: ReDeBug: finding unpatched code clones in entire os distributions. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 48–62 (2012)Google Scholar
  25. 25.
    Johns, M., Lekies, S.: Biting the hand that serves you: a closer look at client-side flash proxies for cross-domain requests. In: Holz, T., Bos, H. (eds.) DIMVA 2011. LNCS, vol. 6739, pp. 85–103. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  26. 26.
    Kapravelos, A., Shoshitaishvili, Y., Cova, M., Kruegel, C., Vigna, G.: Revolver: an automated approach to the detection of evasive web-based malware. In: Proceedings of USENIX Security Symposium, pp. 637–651, August 2013Google Scholar
  27. 27.
    Kolbitsch, C., Livshits, B., Zorn, B., Seifert, C.: Rozzle: de-cloaking internet malware. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 443–457 (2012)Google Scholar
  28. 28.
    Laskov, P., Šrndić, N.: Static detection of malicious javascript-bearing PDF documents. In: Proceedings of Annual Computer Security Applications Conference (ACSAC), pp. 373–382 (2011)Google Scholar
  29. 29.
    Louw, M.T., Thotta, K., Venkatakrishnan, V.N.: AdJail: practical enforcement of confidentiality and integrity policies on web advertisments. In: Proceedings of USENIX Security Symposium, pp. 371–388 (2010)Google Scholar
  30. 30.
    Moser, A., Kruegel, C., Kirda, E.: Exploring multiple execution paths for malware analysis. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 231–245 (2007)Google Scholar
  31. 31.
    Nair, S.K., Simpson, P.N.D., Crispo, B., Tanenbaum, A.S.: A virtual machine based information flow control system for policy enforcement. Electron. Notes Theor. Comput. Sci. (ENTCS) 197(1), 3–16 (2008)CrossRefGoogle Scholar
  32. 32.
    Özkan, S.: CVE Details. http://www.cvedetails.com. Accessed April 2016
  33. 33.
    Perdisci, R., Ariu, D., Fogla, P., Giacinto, G., Lee, W.: McPAD: a multiple classifier system for accurate payload-based anomaly detection. Comput. Netw. 5(6), 864–881 (2009)CrossRefzbMATHGoogle Scholar
  34. 34.
    Pignotti, A.: Lightspark. https://github.com/lightspark. Accessed April 2016
  35. 35.
    Ratanaworabhan, P., Livshits, B., Zorn, B.: Nozzle: a defense against heap-spraying code injection attacks. In: Proceedings of USENIX Security Symposium, pp. 169–186 (2009)Google Scholar
  36. 36.
    Saxena, P., Akhawe, D., Hanna, S., Mao, F., McCamant, S., Song, D.: A symbolic execution framework for javascript. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 513–528 (2010)Google Scholar
  37. 37.
    Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)zbMATHGoogle Scholar
  38. 38.
    Sedgewick, R., Wayne, K.: Algorithms, 4th edn. Addison-Wesley, Boston (2011)Google Scholar
  39. 39.
    Shafiq, M.Z., Khayam, S.A., Farooq, M.: Embedded malware detection using markov n-grams. In: Zamboni, D. (ed.) DIMVA 2008. LNCS, vol. 5137, pp. 88–107. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  40. 40.
    Stolfo, S.J., Wang, K., Li, W.-J.: Towards stealthy malware detection. In: Christodorescu, M., Jha, S., Maughan, D., Song, D., Wang, C. (eds.) Malware Detection, pp. 231–249. Springer, USA (2007)CrossRefGoogle Scholar
  41. 41.
    Suen, C.: N-gram statistics for natural language understanding, text processing. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 164–172 (1979)CrossRefGoogle Scholar
  42. 42.
    Systems, A.: Adobe Flash runtimes: Statistics. http://www.adobe.com/products/flashruntimes/statistics.html. Accessed April 2016
  43. 43.
    van Acker, S., Nikiforakis, N., Desmet, L., Joosen, W., Piessens, F.: FlashOver: automated discovery of cross-site scripting vulnerabilities in rich internet applications. In: Proceedings of ACM Symposium on Information, Computer and Communications Security (ASIACCS) (2012)Google Scholar
  44. 44.
    Van Overveldt, T., Kruegel, C., Vigna, G.: FlashDetect: actionscript 3 malware detection. In: Balzarotti, D., Stolfo, S.J., Cova, M. (eds.) RAID 2012. LNCS, vol. 7462, pp. 274–293. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  45. 45.
    Šrndić, N., Laskov, P.: Detection of malicious PDF files based on hierarchical document structure. In: Proceedings of Network and Distributed System Security Symposium (NDSS) (2013)Google Scholar
  46. 46.
    Wagner, D., Soto, P.: Mimicry attacks on host based intrusion detection systems. In: Proceedings of ACM Conference on Computer and Communications Security (CCS), pp. 255–264 (2002)Google Scholar
  47. 47.
    Wang, K., Parekh, J.J., Stolfo, S.J.: Anagram: a content anomaly detector resistant to mimicry attack. In: Zamboni, D., Kruegel, C. (eds.) RAID 2006. LNCS, vol. 4219, pp. 226–248. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  48. 48.
    Wilhelm, J., Chiueh, T.: A forced sampled execution approach to kernel rootkit identification. In: Kruegel, C., Lippmann, R., Clark, A. (eds.) RAID 2007. LNCS, vol. 4637, pp. 219–235. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  49. 49.
    Wook Oh, J.: AVM inception - how we can use AVM instrumentation in a beneficial way. In: Shmoocon (2012)Google Scholar
  50. 50.
    Wressnegger, C., Boldewin, F., Rieck, K.: Deobfuscating embedded malware using probable-plaintext attacks. In: Stolfo, S.J., Stavrou, A., Wright, C.V. (eds.) RAID 2013. LNCS, vol. 8145, pp. 164–183. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christian Wressnegger
    • 1
    Email author
  • Fabian Yamaguchi
    • 1
  • Daniel Arp
    • 1
  • Konrad Rieck
    • 1
  1. 1.Institute of System SecurityTU BraunschweigBraunschweigGermany

Personalised recommendations