Enhancing Industrial IoT Security: Utilizing Blockchain-Assisted Deep Federated Learning for Collaborative Intrusion Detection

Abstract

The Industrial Internet of Things (IIoT) is a rapidly evolving features with multiple applications, including critical infrastructure. Privacy policies are required to preserve the protection of user data in the threat intelligence community. Blockchain is a modern technology which used recently to provide more secure storage and efficiency.  In this research, Blockchain Assisted Deep Federated Learning (BC_DFL) system is used to detect intruders. The three key processes used in the proposed intrusion detection architecture are data collection, pre-processing and intrusion detection. Data normalization, reduction, cleaning and transformation are used in pre-processing to remove extraneous information and improve data quality. This pre-processed data is sent to the Blockchain Assisted Deep Federated Learning (BC_DFL) system for intrusion detection. To detect intruders, the federated learning-based Capsule Auto-Encoder (FL_CAE) architecture first learns the properties from the inputs. Blockchain technology (BCTech) is not only used for storage but also improves security by eliminating the possibility of threatening node and individual server failure. The ToN_IoT and UNSW-NB15 data sets are used in the implementation and performance evaluation study. The proposed model is evaluated using existing in the Results section. In the UNSW-NB15 dataset, proposed model achieved an accuracy, precision, recall and F1 score of 97.26%, 97.28%, 96.89% and 96.96% respectively as well as in ToN_IoT data set proposed model achieved an accuracy, precision, recall and F1 score of 95.74%, 99.54 %, 99.49% and 99.24%, respectively. The execution of the proposed approach takes 2.31 seconds in the UNSW-NB15 dataset and 1.66 seconds in the ToN_IoT dataset. Blockchain offers a transparent and impenetrable ledger for transaction recording and verification. Within the framework of cooperative intrusion detection, it guarantees a secure and reliable exchange of threat intelligence and detection models between various users of IIoT ecosystem.