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Title	Block Chain And Deep Learning Based Secure Communication Using Sae-Lstm &Salp Swarm Optimizer For Multivariate Industrial Iot-Oriented Infrastructure
ID_Doc	12333
Authors	MathalaiRaj J.; Sivaranjani S.; Rajalakshmi J.; Jayachandran T.; Maheswari M.; Kavin Kumar K.; Chandrasekaran G.
Year	2024
Published	2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
DOI	http://dx.doi.org/10.1109/ICCCNT61001.2024.10725075
Abstract	The rapid development of the Industrial Internet of Things (IIoT) imposes the needs for digitization of industrial procedures so as to enhance the efficiency of the network. Cyber-Physical System (CPS) has important role in applications of industries and infrastructure towards IoT. There are several challenges in IIoT environment such as centralization, scalability, privacy, security and communication latency. To overcome these issues, Deep learning based Sparse Autoencoder-Long Short-Term Memory (SAE-LSTM) with Salp swarm optimizer SSA based IoT-oriented infrastructure is used to detect the attacks. The major objective of this work is to propose a deep learning and blockchain based smart contract technique for a secure communication in smart city using Salp Swarm Optimizer algorithm for determining data privacy and security for the IIoT's application service. Blockchain & data aggregation with Proof-of-Work (PoW) generates a distributed network for communication phase of CPS. The protocols to forward the data in the network is established by Software-Defined Networking (SDN). Smart contract is employed to execute the transaction. The suggested technique is implemented and the performance is carried out by estimating Accuracy, Precision, Recall and F1 Score. © 2024 IEEE.
Author Keywords	Blockchain and data aggregation with Proof-of-Work (PoW); Decision Sparse Autoencoder-Long Short-Term Memory (DSAE-LSTM); Industrial IoT; Salp swarm optimizer SSA; Smart contract

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