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    A high-performance virtual machine filesystem monitor in cloud-assisted cognitive IoT

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    Name:
    1804.01633v1.pdf
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    1.375Mb
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    Genre
    Pre-print
    Date
    2018-11-01
    Author
    Zhan, D
    Ye, L
    Zhang, H
    Fang, B
    Li, H
    Liu, Y
    Du, X
    Guizani, M
    Subject
    Filesystem monitor
    Minimized scanning
    Hooking backend driver
    Copy-on-write image analysis
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/4225
    
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    DOI
    10.1016/j.future.2018.05.055
    Abstract
    © 2018 Elsevier B.V. Cloud-assisted Cognitive Internet of Things has powerful data analytics abilities based on the computing and data storage capabilities of cloud virtual machines, which makes protecting virtual machine filesystem very important for the whole system security. Agentless periodic filesystem monitors are optimal solutions to protect cloud virtual machines because of the secure and low-overhead features. However, most of the periodic monitors usually scan all of the virtual machine filesystem or protected files in every scanning poll, so lots of secure files are scanned again and again even though they are not corrupted. In this paper, we propose a novel agentless periodic filesystem monitor framework for virtual machines with different image formats to improve the performance of agentless periodic monitors. Our core idea is to minimize the scope of the scanning files in both file integrity checking and virus detection. In our monitor, if a file is considered secure, it will not be scanned when it has not been modified. Since our monitor only scans the newly created and modified files, it can check fewer files than other filesystem monitors. To that end, we propose two monitor methods for different types of virtual machine disks to reduce the number of scanning files. For virtual machine with single disk image, we hook the backend driver to capture the disk modification information. For virtual machine with multiple copy-on-write images, we leverage the copy-on-write feature of QCOW2 images to achieve the disk modification analysis. In addition, our system can restore and remove the corrupted files. The experimental results show that our system is effective for both Windows and Linux virtual machines with different image formats and can reduce the number of scanning files and scanning time.
    Citation to related work
    Elsevier BV
    Has part
    Future Generation Computer Systems
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    For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/4207
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