A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks

【Author】 Alkadi, Osama; Moustafa, Nour; Turnbull, Benjamin; Choo, Kim-Kwang Raymond

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】There has been significant research in incorporating both blockchain and intrusion detection to improve data privacy and detect existing and emerging cyberattacks, respectively. In these approaches, learning-based ensemble models can facilitate the identification of complex malicious events and concurrently ensure data privacy. Such models can also be used to provide additional security and privacy assurances during the live migration of virtual machines (VMs) in the cloud and to protect Internet-of-Things (IoT) networks. This would allow the secure transfer of VMs between data centers or cloud providers in real time. This article proposes a deep blockchain framework (DBF) designed to offer security-based distributed intrusion detection and privacy-based blockchain with smart contracts in IoT networks. The intrusion detection method is employed by a bidirectional long short-term memory (BiLSTM) deep learning algorithm to deal with sequential network data and is assessed using the data sets of UNSW-NB15 and BoT-IoT. 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 peer privacy-preserving intrusion detection techniques, and the experimental outcomes reveal that DBF outperforms the other competing models. The framework has the potential to be used as a decision support system that can assist users and cloud providers in securely migrating their data in a timely and reliable manner.

【Keywords】Cloud computing; Contracts; Intrusion detection; Internet of Things; Data privacy; Cryptography; Blockchain; cloud systems; collaborative intrusion detection; deep learning; learning-based ensemble model; privacy preservation

【发表时间】2021 44727

【收录时间】2022-01-02

【文献类型】

【主题类别】

--