Neural Network Stealing via Meltdown

Hoyong Jeong, Dohyun Ryu, Junbeom Hur

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    Deep learning services are now deployed in various fields on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multitenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep learning service with 92.875% accuracy and 1.325kB/s extraction speed.

    Original languageEnglish
    Title of host publication35th International Conference on Information Networking, ICOIN 2021
    PublisherIEEE Computer Society
    Pages36-38
    Number of pages3
    ISBN (Electronic)9781728191003
    DOIs
    Publication statusPublished - 2021 Jan 13
    Event35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of
    Duration: 2021 Jan 132021 Jan 16

    Publication series

    NameInternational Conference on Information Networking
    Volume2021-January
    ISSN (Print)1976-7684

    Conference

    Conference35th International Conference on Information Networking, ICOIN 2021
    Country/TerritoryKorea, Republic of
    CityJeju Island
    Period21/1/1321/1/16

    Bibliographical note

    Funding Information:
    This work was supported by Institute of Information communications Technology Planning Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-00533, Research on CPU vulnerability detection and validation) (No.2019-0-01697, Development of Automated Vulnerability Discovery Technologies for Blockchain Platform Security) (IITP-2020-0-01819, ICT Creative Consilience program).

    Publisher Copyright:
    © 2021 IEEE.

    Keywords

    • Meltdown
    • cloud computing
    • deep learning
    • neural network stealing

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems

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