Retraction Notice: Blockchain and Deep Learning for Secure Communication in Digital Twin Empowered Industrial IoT Network

IF 7.9 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Prabhat Kumar;Randhir Kumar;Abhinav Kumar;A. Antony Franklin;Sahil Garg;Satinder Singh
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引用次数: 0

Abstract

The rapid expansion of the Industrial Internet of Things (IIoT) necessitates the digitization of industrial processes in order to increase network efficiency. The integration of Digital Twin (DT) with IIoT digitizes physical objects into virtual representations to improve data analytics performance. Nevertheless, DT empowered IIoT generates a massive amount of data that is mostly sent to the cloud or edge servers for real-time analysis. However, unreliable public communication channels and lack of trust among participating entities causes various types of threats and attacks on the ongoing communication. Motivated from the aforementioned discussion, we present a blockchain and Deep Learning (DL) integrated framework for delivering decentralized data processing and learning in IIoT network. The framework first present a new DT model that facilitates construction of a virtual environment to simulate and replicate security-critical processes of IIoT. Second, we propose a blockchain-based data transmission scheme that uses smart contracts to ensure integrity and authenticity of data. Finally, the DL scheme is designed to apply the Intrusion Detection System (IDS) against valid data retrieved from blockchain. In DL scheme, a Long Short Term Memory-Sparse AutoEncoder (LSTMSAE) technique is proposed to learn the spatial-temporal representation. The extracted characteristics are further used by the proposed Multi-Head Self-Attention (MHSA)-based Bidirectional Gated Recurrent Unit (BiGRU) algorithm to learn long-distance features and accurately detect attacks. The practical implementation of our proposed framework proves considerable enhancement of communication security and data privacy in DT empowered IIoT network.
撤回通知:区块链和深度学习在数字孪生驱动的工业物联网网络中的安全通信
工业物联网(IIoT)的快速发展要求工业过程的数字化,以提高网络效率。数字孪生(DT)与工业物联网的集成将物理对象数字化为虚拟表示,以提高数据分析性能。然而,DT授权的工业物联网生成了大量数据,这些数据主要发送到云或边缘服务器进行实时分析。然而,由于公共通信渠道不可靠,参与主体之间缺乏信任,导致正在进行的通信受到各种威胁和攻击。受上述讨论的启发,我们提出了一个区块链和深度学习(DL)集成框架,用于在IIoT网络中提供分散的数据处理和学习。该框架首先提出了一个新的DT模型,该模型有助于构建虚拟环境来模拟和复制工业物联网的安全关键过程。其次,我们提出了一种基于区块链的数据传输方案,该方案使用智能合约来确保数据的完整性和真实性。最后,设计了入侵检测系统(IDS)对从区块链检索到的有效数据的入侵检测方案。在深度学习方案中,提出了一种长短期记忆稀疏自动编码器(LSTMSAE)技术来学习时空表示。将提取的特征进一步用于基于多头自关注(MHSA)的双向门控循环单元(BiGRU)算法,学习远程特征,准确检测攻击。我们提出的框架的实际实施证明了DT授权IIoT网络中通信安全和数据隐私的显着增强。
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
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