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

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.