The Effect of Label-Flipping attack on Different Mobile Machine Learning Classifiers

Alanoud Almemari, Raviha Khan, Chan Yeob Yeun

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    AI technology is widely used in different fields due to the effectiveness and accurate results that have been achieved. The diversity of usage attracts many attackers to attack AI systems to reach their goals. One of the most important and powerful attacks launched against AI models is the label-flipping attack. This attack allows the attacker to compromise the integrity of the dataset, where the attacker is capable of degrading the accuracy of ML models or generating specific output that is targeted by the attacker. Therefore, this paper studies the robustness of several Machine Learning models against targeted and non-targeted label-flipping attacks against the dataset during the training phase. Also, it checks the repeatability of the results obtained in the existing literature. The results are observed and explained in the domain of the cyber security paradigm.

    Original languageBritish English
    Title of host publication2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9798350335644
    DOIs
    StatePublished - 2023
    Event2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023 - Dubai, United Arab Emirates
    Duration: 7 Mar 20238 Mar 2023

    Publication series

    Name2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

    Conference

    Conference2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
    Country/TerritoryUnited Arab Emirates
    CityDubai
    Period7/03/238/03/23

    Keywords

    • Artificial Inelegant (AI)
    • Blockchain
    • label-flipping attack
    • Machine learning (ML)
    • poisoning attack
    • Support vector machine (SVM)

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