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When Side Channel Becomes Good: Kernel Malware Attack Investigation

  • Conference paper
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Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11633))

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Abstract

Security and privacy issues have been a concern and have become one of the main factors hindering the promotion and popularization of cloud computing. In recent years, cache side channel attack are presented by many researchers to crack cryptographic algorithms (e.g., AES, RSA), bypass ASLR and etc. Cache side channel had been considered as a hacking tool to conduct harmful activities on victim systems. However, from a defender’s perspective, cache side channel can also be employed to explore valuable information. Our paper employs cache side channel to obtain a deep insight on what kind of behaviors kernel malware may conduct. In specific, we propose a novel approach to conduct kernel malware attack investigation with Flush+Reload cache side channel. We have built a proof-of-concept prototype and designed some case studies to conduct extensive experiments. The evaluation results show that our system is capable of understanding what kind of behaviors kernel malware may conduct correctly.

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Acknowledgement

We would like to thank the anonymous reviewers for their insightful comments that greatly helped to improve this paper. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of these agencies.

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Correspondence to Chonghua Wang .

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Yin, L., Wang, C., Li, J., Yin, R., Jiao, Y., Jiang, H. (2019). When Side Channel Becomes Good: Kernel Malware Attack Investigation. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_49

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  • DOI: https://doi.org/10.1007/978-3-030-24265-7_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24264-0

  • Online ISBN: 978-3-030-24265-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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