Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication

Mohamad Wazzeh, Mohamad Arafeh, Hakima Ould-Slimane, Chamseddine Talhi, Azzam Mourad, Hadi Otrok

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

    Abstract

    In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security.

    Original languageBritish English
    Title of host publication2023 7th Cyber Security in Networking Conference, CSNet 2023
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages114-119
    Number of pages6
    ISBN (Electronic)9798350342871
    DOIs
    StatePublished - 2023
    Event7th Cyber Security in Networking Conference, CSNet 2023 - Montreal, Canada
    Duration: 16 Oct 202318 Oct 2023

    Publication series

    Name2023 7th Cyber Security in Networking Conference, CSNet 2023

    Conference

    Conference7th Cyber Security in Networking Conference, CSNet 2023
    Country/TerritoryCanada
    CityMontreal
    Period16/10/2318/10/23

    Keywords

    • Clusters
    • Continuous authentication
    • Cybersecurity
    • Federated learning
    • Internet Of Things (IoT)
    • Neural network
    • Split learning

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