Optimised deep learning-based intrusion detection using Ethereum blockchain framework for secure data sharing

Abstract

This paper proposes a secure data-sharing model that utilises a blockchain-based secure framework and a deep learning-based intrusion detection model to ensure patient privacy and provide personalised healthcare services. The proposed model consists of two phases: validation and verification. In the validation phase, electronic health record (EHR) data is uploaded to an Ethereum blockchain, encrypted using improved elliptic curve cryptography (Imp-ECC), and stored in an interplanetary file system (IPFS) within the blockchain. In the verification phase, an optimised deep-learning approach, enhanced capsule-BiLSTM, is used to detect unauthorised users in the network. If an attack is detected, access is denied; otherwise, the user is authorised to access the encrypted data. The proposed model is evaluated using two datasets, EHR and UNSW-NB15. The results show that the proposed model achieves a less encryption time of 198 seconds for the EHR dataset and an accuracy of 97.19% for the UNSW-NB15 dataset.