Abstract
In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.
Highlights
The advancements made in industries, especially large-scale, in recent years have been rapid in the areas of surveillance, transportation, security, and factory automation
With astonishing advancements made in sensor network technologies and wireless communications, considerable number of devices are being presented to Industrial IoT (IIoT) environment in which the raw data is processed and captured locally for data-driven decision-making process
For validating the supremacy of the proposed PPBDL-IIoT technique, a series of simulations was executed on Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were discussed under distinct parameters
Summary
The advancements made in industries, especially large-scale, in recent years have been rapid in the areas of surveillance, transportation, security, and factory automation. With astonishing advancements made in sensor network technologies and wireless communications, considerable number of devices are being presented to IIoT environment in which the raw data is processed and captured locally for data-driven decision-making process. Blockchain has a problem with scalability, energy consumption, privacy, efficiency, and security whereas AI overcomes a few problems like effectiveness and interpretability Though both techniques are distinct areas of research, it could be associated with one another so as to have the advantage of natural incorporation. The current research paper develops a new Privacy Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment in order to identify the presence of intrusions in network.
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