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Mixed-Mode Malware and Its Analysis

Published:09 December 2014Publication History

ABSTRACT

Mixed-mode malware contains user-mode and kernel-mode components that are interdependent. Such malware exhibits its main malicious payload only after it succeeds at corrupting the OS kernel. Such malware may further actively attack or subvert malware analysis components. Current malware analysis techniques are not effective against mixed-mode malware. To overcome the limitations of current techniques, we present an approach that combines whole-system analysis with outside-the-guest virtual machine introspection. We implement this approach in the SEMU tool for Windows. In our experiments SEMU could successfully analyze several mixed-mode malware samples that evade current analysis approaches. The runtime overhead of SEMU is in line with the most closely related dynamic analysis tools TEMU and Ether.

References

  1. S. Bahram et al. DKSM: Subverting virtual machine introspection for fun and profit. In SRDS, pages 82--91. IEEE, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. D. Balzarotti, M. Cova, C. Karlberger, E. Kirda, C. Krügel, and G. Vigna. Efficient detection of split personalities in malware. In NDSS. The Internet Society, 2010.Google ScholarGoogle Scholar
  3. D. Bartholomew. QEMU: A multihost, multitarget emulator. Linux Journal, (145), 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. U. Bayer, C. Krügel, and E. Kirda. TTAnalyze: A tool for analyzing malware. In EICAR. EICAR, 2006.Google ScholarGoogle Scholar
  5. U. Bayer, A. Moser, C. Krügel, and E. Kirda. Dynamic analysis of malicious code. Journal in Computer Virology, 2(1):67--77, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  6. M. Carbone, W. Cui, L. Lu, W. Lee, M. Peinado, and X. Jiang. Mapping kernel objects to enable systematic integrity checking. In CCS, pages 555--565. ACM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D. Chisnall. The definitive guide to the Xen hypervisor. Pearson Education, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Consumer Reports. Online exposure. Consumer Reports Magazine, June 2011.Google ScholarGoogle Scholar
  9. A. Dinaburg, P. Royal, M. I. Sharif, and W. Lee. Ether: Malware analysis via hardware virtualization extensions. In CCS, pages 51--62. ACM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. B. Dolan-Gavitt, T. Leek, M. Zhivich, J. Giffin, and W. Lee. Virtuoso: Narrowing the semantic gap in virtual machine introspection. In Security and Privacy, pages 297--312. IEEE, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. E. Florio. When malware meets rootkits. In Virus Bulletin. Virus Bulletin, 2005.Google ScholarGoogle Scholar
  12. Y. Fu and Z. Lin. Space traveling across VM: Automatically bridging the semantic gap in virtual machine introspection via online kernel data redirection. In Security and Privacy, pages 586--600. IEEE, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Y. Fu and Z. Lin. Bridging the semantic gap in virtual machine introspection via online kernel data redirection. ACM TISSEC, 16(2):7:1--7:29, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. T. Garfinkel and M. Rosenblum. A virtual machine introspection based architecture for intrusion detection. In NDSS. The Internet Society, 2003.Google ScholarGoogle Scholar
  15. I. Habib. Virtualization with KVM. Linux Journal, (166), 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. T. Holz, M. Steiner, F. Dahl, E. Biersack, and F. C. Freiling. Measurements and mitigation of peer-to-peer-based botnets: A case study on storm worm. In LEET. USENIX, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. X. Jiang and X. Wang. "Out-of-the-box" monitoring of VM-based high-interaction honeypots. In RAID, pages 198--218. Springer, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. X. Jiang, X. Wang, and D. Xu. Stealthy malware detection through VMM-based "out-of-the-box" semantic view reconstruction. In CCS, pages 128--138. ACM, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. A. Kapoor and R. Mathur. Predicting the future of stealth attacks. In Virus Bulletin Conference, 2011.Google ScholarGoogle Scholar
  20. C. Kolbitsch, E. Kirda, and C. Kruegel. The power of procrastination: detection and mitigation of execution-stalling malicious code. In CCS, pages 285--296. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. A. Lanzi, M. Sharif, and W. Lee. K-tracer: A system for extracting kernel malware behavior. In NDSS. The Internet Society, 2009.Google ScholarGoogle Scholar
  22. A. Moser, C. Krügel, and E. Kirda. Exploring multiple execution paths for malware analysis. In Security and Privacy, pages 231--245. IEEE, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. M. Neugschwandtner, C. Platzer, P. Comparetti, and U. Bayer. dAnubis - dynamic device driver analysis based on virtual machine introspection. In DIMVA, pages 41--60. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. N. A. Quynh and K. Suzaki. Virt-ICE: Next-generation debugger for malware analysis. Black Hat Briefings USA, July 2010.Google ScholarGoogle Scholar
  25. R. Riley, X. Jiang, and D. Xu. Guest-transparent prevention of kernel rootkits with VMM-based memory shadowing. In RAID, pages 1--20. Springer, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. R. Riley, X. Jiang, and D. Xu. Multi-aspect profiling of kernel rootkit behavior. In EuroSys, pages 47--60. ACM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. E. Russinovich and D. A. Solomon. Microsoft Windows Internals: Microsoft Windows Server 2003, Windows XP, and Windows 2000. Microsoft Press, fourth edition, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. S. B. Schreiber. Undocumented Windows 2000 secrets: A programmer's cookbook. Addison-Wesley, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. A. Seshadri, M. Luk, N. Qu, and A. Perrig. Secvisor: A tiny hypervisor to provide lifetime kernel code integrity for commodity OSes. In SOSP, pages 335--350. ACM, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. M. I. Sharif, W. Lee, W. Cui, and A. Lanzi. Secure in-vm monitoring using hardware virtualization. In CCS, pages 477--487. ACM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. D. X. Song et al. BitBlaze: A new approach to computer security via binary analysis. In ICISS, pages 1--25. Springer, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. A. Srivastava and J. Giffin. Efficient protection of kernel data structures via object partitioning. In ACSAC, pages 429--438. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Z. Wang, X. Jiang, W. Cui, and P. Ning. Countering kernel rootkits with lightweight hook protection. In CCS, pages 545--554. ACM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. C. Willems, T. Holz, and F. C. Freiling. Toward automated dynamic malware analysis using CWSandbox. IEEE Security and Privacy, 5(2):32--39, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. C. Xuan, J. Copeland, and R. Beyah. Toward revealing kernel malware behavior in virtual execution environments. In RAID, pages 304--325. Springer, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. L.-K. Yan, M. Jayachandra, M. Zhang, and H. Yin. V2E: Combining hardware virtualization and software emulation for transparent and extensible malware analysis. In VEE, pages 227--237. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. H. Yin, Z. Liang, and D. Song. Hookfinder: Identifying and understanding malware hooking behaviors. In NDSS. The Internet Society, 2008.Google ScholarGoogle Scholar
  38. H. Yin, P. Poosankam, S. Hanna, and D. X. Song. Hookscout: Proactive binary-centric hook detection. In DIMVA, pages 1--20. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. H. Yin, D. Song, M. Egele, C. Kruegel, and E. Kirda. Panorama: Capturing system-wide information flow for malware detection and analysis. In CCS, pages 116--127. ACM, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Mixed-Mode Malware and Its Analysis

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      • Published in

        cover image ACM Other conferences
        PPREW-4: Proceedings of the 4th Program Protection and Reverse Engineering Workshop
        December 2014
        77 pages
        ISBN:9781605586373
        DOI:10.1145/2689702

        Copyright © 2014 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 9 December 2014

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        • research-article
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        Acceptance Rates

        PPREW-4 Paper Acceptance Rate7of14submissions,50%Overall Acceptance Rate21of36submissions,58%

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