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A Collaborative Intrusion Detection System Using Deep Blockchain Framework for Securing Cloud Networks

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Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1250))

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Abstract

Security solutions, especially intrusion detection and blockchain, have been individually employed in the cloud for detecting cyber threats and preserving private data. Both solutions demand ensembled models-based learning that can alert the campaign of complex malicious events and concurrently accomplish data privacy. Such models would also provide additional security and privacy to the live migration of Virtual Machines (VMs) in the cloud. This would allow the secure transfer of one or more VMs between datacentres or cloud providers in real-time. This paper proposes a Deep Blockchain Framework (DBF) designed to offer security-based distributed intrusion detection and privacy-based blockchain with smart contracts in the cloud. The intrusion detection method is employed yet using a Bidirectional Long Short-Term Memory (BiLSTM) deep learning algorithm to deal with sequential network data and is assessed using the dataset of UNSW-NB15. The Privacy-based blockchain and smart contract methods are developed using the Ethereum library to provide privacy to the distributed intrusion detection engines. The DBF framework is compared with compelling privacy-preserving intrusion detection models, and the empirical results reveal that DBF outperforms the compelling models. The framework has the potential to be used as a decision support system that can assist users and cloud providers in securely and timely migrating their data in a fast and reliable manner.

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Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales at ADFA, Northcott Dr, Campbell ACT 2612, Canberra, Australia

    Osama Alkadi, Nour Moustafa & Benjamin Turnbull

Authors
  1. Osama Alkadi
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  2. Nour Moustafa
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  3. Benjamin Turnbull
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Corresponding author

Correspondence to Osama Alkadi .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Alkadi, O., Moustafa, N., Turnbull, B. (2021). A Collaborative Intrusion Detection System Using Deep Blockchain Framework for Securing Cloud Networks. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_41

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