A blockchain-based decentralized machine learning framework for collaborative intrusion detection within UAVs

Khan, Ammar Ahmed and Khan, Muhammad Mubashir and Khan, Kashif Mehboob and Arshad, Junaid and Ahmad, Farhan (2021) A blockchain-based decentralized machine learning framework for collaborative intrusion detection within UAVs. Computer Networks, 196. ISSN 1389-1286

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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 centralised 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 utilise machine learning techniques in order to take decisions in human absence. In this regard, we propose to enhance the performance of UAVs with a decentralised 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 decentralised 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 decentralised machine learning approach in UAVs and other similar applications.

Item Type: Article
Identification Number: https://doi.org/10.1016/j.comnet.2021.108217
31 May 2021Accepted
11 June 2021Published Online
Uncontrolled Keywords: Unmanned aerial vehicles UAV Blockchain Machine learning Decentralized machine learning Collaborative intrusion detection
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Junaid Arshad
Date Deposited: 29 Sep 2021 14:59
Last Modified: 11 Jun 2023 03:00
URI: https://www.open-access.bcu.ac.uk/id/eprint/12222

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