• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Electrical Engineering
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Electrical Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Federated Learning with Kalman Filter for Intrusion Detection in IoT Environment

    View/Open
    Federated_Learning_with_Kalman_Filter_for_Intrusion_Detection_in_IoT_Environment.pdf (1004.Kb)
    Date
    2024
    Author
    Almesleh, Ziad
    Gouissem, Ala
    Hamila, Ridha
    Metadata
    Show full item record
    Abstract
    Enhancing the IoT (Internet of Things) network for reliability prompted a heightened focus on device security, given their diverse characteristics and sensitive data exchange. Federated Learning (FL) gained attention for its collaborative approach, sharing only local models, not data, among devices in IoT networks. Additionally, the non-i.i.d. data distribution complexity adds another layer to the network's challenges. In this article, we propose FedKF as a model approach for a federated learning algorithm with KF (Kalman Filter). This approach improves the performance of the IDS (Intrusion Detection System), especially for IoT data. In this model, each edge client trains the data locally to form a local model, which is then aggregated on a central server to create a global model. The FedKF aggregation algorithm employs a KF to predict and estimate the aggregation weight, where the prediction is based on the current measured weight of aggregated global models and the previous model weight. Furthermore, by selecting clients and allowing only selected devices to participate in the training process, the overall energy consumption can be reduced. Therefore, it's essential to balance energy savings with the performance of the federated learning model, ensuring that the model remains accurate and effective despite reduced participation. The experimental results demonstrate the model's performance across different IoT datasets, compare the results with the average FL model, and verify the noticeable improvement in accuracy and communication loss. The model also shows the effect of client selection on the model's performance.
    DOI/handle
    http://dx.doi.org/10.1109/ENERGYCON58629.2024.10488796
    http://hdl.handle.net/10576/57842
    Collections
    • Electrical Engineering [‎2823‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policiesUser guides FAQs

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video