A Blockchain-based Decentralized Machine Learning Framework for Collaborative Intrusion Detection within UAVs

Ammar Ahmed Khan, Muhamaad Mubashir Khan, Kashif Mehboob Khan, Junaid Arshad, Farhan Ahmad

    Research output: Contribution to journalArticlepeer-review

    26 Citations (Scopus)
    361 Downloads (Pure)


    UAVs have numerous emerging applications in various domains of life. However, it is extremely challenging to gain the required level of public acceptance of UAVs without proving safety and security for human life. Conventional UAVs mostly depend upon the centralized server to perform data processing with complex machine learning algorithms. In fact, all the conventional cyber attacks are applicable on the transmission and storage of data in UAVs. While their impact is extremely serious because UAVs are highly dependent on smart systems that extensively utilize machine learning techniques in order to take decisions in human absence. In this regard, we propose to enhance the performance of UAVs with a decentralized machine learning framework based on blockchain. The proposed framework has the potential to significantly enhance the integrity and storage of data for intelligent decision making among multiple UAVs. We present the use of blockchain to achieve decentralized predictive analytics and present a framework that can successfully apply and share machine learning models in a decentralized manner. We evaluate our system using collaborative intrusion detection as a case-study in order to highlight the feasibility and effectiveness of using blockchain based decentralized machine learning approach in UAVs and other similar applications.
    Original languageEnglish
    Article number108217
    JournalComputer Networks
    Early online date11 Jun 2021
    Publication statusPublished - 4 Sept 2021

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Computer Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Networks, 196, (2021) DOI: 10.1016/j.comnet.2021.108217

    © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/


    • Unmanned aerial vehicles
    • UAV
    • Blockchain
    • Decentralized machine learning
    • Collaborative intrusion detection
    • Machine learning

    ASJC Scopus subject areas

    • Computer Networks and Communications


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