Abstract
Provenance of system subjects (e.g., processes) and objects (e.g., files) are very useful for many forensics tasks. In our analysis and comparison of existing Linux provenance tracing systems, we found that most systems assume the Linux kernel to be in the trust base, making these systems vulnerable to kernel level malware. To address this problem, we present HProve, a hypervisor level provenance tracing system to reconstruct kernel malware attack story. It monitors the execution of kernel functions and sensitive objects, and correlates the system subjects and objects to form the causality dependencies for the attacks. We evaluated our prototype on 12 real world kernel malware samples, and the results show that it can correctly identify the provenance behaviors of the kernel malware.
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References
Unmasking kernel exploits. https://www.lastline.com/labsblog/unmasking-kernel-exploits/
Aristide, F., Andrea, L., Davide, B., Engin, K.: Hypervisor-based malware protection with AccessMiner. Comput. Secur. 52, 33–50 (2015)
Bahram, S., Jiang, X., Wang, Z., Grace, M., Li, J., Srinivasan, D., Rhee, J., Xu, D.: DKSM: subverting virtual machine introspection for fun and profit. In: SRDS, pp. 82–91 (2010)
Bates, A., Tian, D., Butler, K., Moyer, T.: Trustworthy whole-system provenance for the Linux kernel. In: USENIX Security, pp. 319–334 (2015)
Carbone, M., Cui, W., Lu, L., Lee, W., Peinado, M., Jiang, X.: Mapping kernel objects to enable systematic integrity checking. In: CCS, pp. 555–565 (2009)
Dolan-Gavitt, B., Hodosh, J., Hulin, P., Leek, T., Whelan, R.: Repeatable reverse engineering with panda. In: Proceedings of 5th Program Protection and Reverse Engineering Workshop, pp. 4:1–4:11 (2015)
Dolan-Gavitt, B., Leek, T., Hodosh, J., Lee, W.: Tappan zee (north) bridge: mining memory accesses for introspection. In: CCS, pp. 839–850 (2013)
Garfinkel, T., Rosenblum, M.: A virtual machine introspection based architecture for intrusion detection. In: NDSS, pp. 191–206 (2003)
Jain, B., Baig, M.B., Zhang, D., Porter, D.E., Sion, R.: SoK: introspections on trust and the semantic gap. In: Proceedings of 35th IEEE S&P, pp. 605–620 (2014)
Jiang, X., Wang, X., Xu, D.: Stealthy malware detection through VMM-based out-of-the-box semantic view reconstruction. In: CCS, pp. 128–138 (2007)
Lanzi, A., Sharif, M., Lee, W.: K-tracer: a system for extracting kernel malware behavior. In: NDSS (2009)
Lee, K., Zhang, X., Xu, D.: High accuracy attack provenance via binary-based execution partition. In: NDSS (2013)
Lee, K., Zhang, X., Xu, D.: LogGC: garbage collecting audit log. In: CCS, pp. 1005–1016 (2013)
Li, J., Wang, Z., Jiang, X., Grace, M., Bahram, S.: Defeating return-oriented rootkits with “return-less” kernels. In: EuroSys, pp. 195–208 (2010)
Liangnd, Z., Yin, H., Song, D.: HookFinder: identifying and understanding malware hooking behaviors. In: NDSS, pp. 41–57 (2008)
Ma, S., Zhang, X., Xu, D.: ProTracer: towards practical provenance tracing by alternating between logging and tainting. In: NDSS (2016)
Pei, K., Gu, Z., Saltaformaggio, B., Ma, S., Wang, F., Zhang, Z., Si, L., Zhang, X., Xu, D.: HERCULE: attack story reconstruction via community discovery on correlated log graph. In: ACSAC, pp. 583–595 (2016)
Pohly, D., McLaughlin, S., McDaniel, P., Butler, K.: Hi-Fi: collecting high-fidelity whole-system provenance. In: ACSAC, pp. 259–268 (2012)
Rhee, J., Xu, D., Riley, R., Jiang, X.: Kernel malware analysis with un-tampered and temporal views of dynamic kernel memory. In: RAID, pp. 178–197 (2010)
Rhee, J., Riley, R., Xu, D., Jiang, X.: Defeating dynamic data kernel rootkit attacks via VMM-based guest-transparent monitoring. In: 2009 International Conference on Availability, Reliability and Security, pp. 74–81 (2009)
Riley, R., Jiang, X., Xu, D.: Guest-transparent prevention of kernel rootkits with VMM-based memory shadowing. In: RAID, pp. 1–20 (2008)
Riley, R., Jiang, X., Xu, D.: Multi-aspect profiling of kernel rootkit behavior. In: EuroSys, pp. 47–60 (2009)
Rudd, E., Rozsa, A., Gunther, M., Boult, T.: A survey of stealth malware: attacks, mitigation measures, and steps toward autonomous open world solutions. IEEE Commun. Surv. Tutor. PP(99), 1–28 (2016)
Wang, Z., Jiang, X., Cui, W., Wang, X.: Countering persistent kernel rootkits through systematic hook discovery. In: RAID, pp. 21–38 (2008)
Xu, Z., Wu, Z., Li, Z., Jee, K., Rhee, J., Xiao, X., Xu, F., Wang, H., Jiang, G.: High fidelity data reduction for big data security dependency analyses. In: CCS, pp. 504–516 (2016)
Xuan, C., Copeland, J., Beyah, R.: Toward revealing kernel malware behavior in virtual execution environments. In: RAID, pp. 304–325 (2009)
Zeng, J., Fu, Y., Lin, Z.: Automatic uncovering of tap points from kernel executions. In: RAID, pp. 49–70 (2016)
Acknowledgement
We would like to thank the anonymous reviewers for their insightful comments that greatly helped improve this paper. This work is a part of the project supported by Beijing Municipal Science Technology Commission (Z161100002616032), Beijing Natural Science Foundation (4172069) and a joint Ph.D program funded by Chinese Academy of Sciences.
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Wang, C., Ma, S., Zhang, X., Rhee, J., Yun, X., Hao, Z. (2018). A Hypervisor Level Provenance System to Reconstruct Attack Story Caused by Kernel Malware. In: Lin, X., Ghorbani, A., Ren, K., Zhu, S., Zhang, A. (eds) Security and Privacy in Communication Networks. SecureComm 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-78813-5_42
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DOI: https://doi.org/10.1007/978-3-319-78813-5_42
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