Abstract:
e Industrial Internet of ings (IIoT) has received signi cant attention from several leading industries like agriculture, mining,
transport, energy, and healthcare. IIoTacts as a vital part of Industry 4.0 that mainly employs machine learning (ML) to investigate
the interconnection and massive quantity of the IIoT data. As the data are generally saved at the cloud server, security and privacy
of the collected data from numerous distributed and heterogeneous devices remain a challenging issue. is article develops a
novel multi-agent system (MAS) with deep learning-based privacy preserving data transmission (BDL-PPDT) scheme for
clustered IIoT environment. e goal of the BDL-PPDT technique is to accomplish secure data transmission in clustered IIoT
environment. e BDL-PPDT technique involves a two-stage process. Initially, an enhanced moth swarm algorithm-based
clustering (EMSA-C) technique is derived to choose a proper set of clusters in the IIoT system and construct clusters. Besides,
multi-agent system is used to enable secure inter-cluster communication. Moreover, multi-head attention with bidirectional long
short-term memory (MHA-BLSTM) model is applied for intrusion detection process. Furthermore, the hyperparameter tuning
process of the MHA-BLSTM model can be carried out by the stochastic gradient descent with momentum (SGDM) model to
improve the detection rate. For examining the promising performance of the BDL-PPDT technique, an extensive comparison
study takes place and the results are assessed under varying measures. A signi cant amount of capital is required. It goes without
saying that one of the most obvious industrial IoT concerns is the high cost of adoption. Secure data storage and management
connectivity failures are common among IoT devices due to the massive amount of data they create. e simulation results
demonstrate the enhanced outcomes of the BDL-PPDT technique over the recent methods. Despite the fact that the o ered BDL PPDT technique has an accuracy of just 98.15 percent, it produces the best feasible outcome. Because of the data analysis
conducted as detailed above, it was determined that the BDL-PPDT technique outperformed the other current techniques on a
range of di erent criteria and was thus recommended.
