DBTP2SF: A deep blockchain‐based trustworthy privacy‐preserving secured framework in industrial internet of things systems,Transactions on Emerging Telecommunications Technologies

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DBTP2SF: A deep blockchain‐based trustworthy privacy‐preserving secured framework in industrial internet of things systems
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2021-01-28 , DOI: 10.1002/ett.4222
Randhir Kumar ₁ , Rakesh Tripathi ₁

Affiliation

By providing ubiquitous connectivity, effective data analytics tools, and better decision support systems for improved market competitiveness, the industrial internet of things (IIoT) promises creative business models in different industrial domains. However, the conventional IIoT architecture can no longer provide adequate support for such an enormous device as the number of nodes, and network size increases. Therefore, several challenges, such as security, privacy, centralization, trust, and integrity prevents faster adaptation of IIoT applications. To address aforementioned challenges, we present a deep blockchain‐based trustworthy privacy‐preserving secured framework (DBTP2SF) for IIoT environment. This framework comprises of three modules, namely, trust management module, a two‐level privacy‐preservation module, and an anomaly detection module. In trustworthiness module, blockchain (BC)‐based address reputation system is proposed. In the two‐level privacy module a BC‐based enhanced proof of work technique is simultaneously applied with AutoEncoder, to transform cyber‐physical system data into a new reduced form that prevents inference and poisoning attacks. In the anomaly detection module, deep neural network is deployed. Finally, due to various limitations of current Cloud‐Fog infrastructure, we present a BC‐interplanetary file systems integrated Cloud‐Fog architecture, namely, BlockCloud and BlockFog to deploy proposed DBTP2SF framework in IIoT environment. The experiment is conducted using IIoT‐based realistic dataset, namely, ToN‐IoT. The performance analysis shows that the proposed approach outperforms using transformed dataset over peer privacy‐preserving intrusion detection strategies, and has obtained accuracy of 98.97%, and detection rate of 93.87%. Finally, we have shown the superiority of DBTP2SF framework over some of the recent state‐of‐art techniques in both non‐BC and BC‐based IIoT system.

中文翻译：

DBTP2SF：工业物联网系统中基于深度区块链的可信赖的隐私保护安全框架

通过提供无处不在的连接性，有效的数据分析工具以及更好的决策支持系统来提高市场竞争力，工业物联网（IIoT）承诺了在不同工业领域中的创新业务模型。但是，随着节点数量的增加和网络规模的扩大，传统的IIoT体系结构无法再为如此庞大的设备提供足够的支持。因此，诸如安全性，隐私性，集中性，信任性和完整性之类的若干挑战阻止了IIoT应用程序的更快适应。为了应对上述挑战，我们为IIoT环境提出了一个基于区块链的深层可信赖的隐私保护安全框架（DBTP2SF）。该框架包含三个模块，即信任管理模块，两级隐私保护模块，以及异常检测模块。在可信赖性模块中，提出了基于区块链（BC）的地址信誉系统。在两级隐私模块中，基于BC的增强型工作量证明技术与AutoEncoder同时应用，以将网络物理系统数据转换为新的简化形式，以防止推理和中毒攻击。在异常检测模块中，部署了深度神经网络。最后，由于当前Cloud-Fog基础架构的种种限制，我们提出了一个BC-星际文件系统集成Cloud-Fog架构，即BlockCloud和BlockFog，以在IIoT环境中部署建议的DBTP2SF框架。该实验是使用基于IIoT的现实数据集，即ToN‐IoT进行的。性能分析表明，该方法在对等隐私保护入侵检测策略上优于使用变换后的数据集，其准确率达98.97％，检测率达93.87％。最后，我们已经展示了DBTP2SF框架在非BC和基于BC的IIoT系统中优于某些最新技术的优势。

更新日期：2021-04-05

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