Collaborative Trust Blockchain Based Unbiased Control Transfer Mechanism for Industrial Automation

Chen, Jianing; Wu, Jun; Liang, Haoran; Mumtaz, Shahid; Li, Jianhua; Igorevich, Kostromitin Konstantin; Bashir, Ali Kashif; Nawaz, Raheel

doi:10.1109/tia.2019.2959550

Cited by 43 publications

(18 citation statements)

References 25 publications

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“…For large-scale industrial systems in the IIoT, efficient command and control are complex and challenging tasks. Computing platforms must be capable of processing and analyzing big industrial data in a timely and secure manner [9], [10]. Furthermore, the capacity and throughput of the system must be high to provide low latency and high reliability of data transmission.…”

Section: Related Work In Iiot Securitymentioning

confidence: 99%

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

et al. 2020

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The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), malicious operation, malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security. INDEX TERMS Artificial neural network, cybersecurity, industrial Internet of Things, random neural network, support vector machine.

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Section: Related Work In Iiot Securitymentioning

confidence: 99%

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

et al. 2020

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“…Furthermore, it helps to enforce a well-founded hierarchy of the linked data sources since the heterogeneously interconnected resources represented by IoT paradigm require a common language to boost the interoperability between the resources as well as offer homogeneous outcomes when multiple resources are queried. Finally, this system must secure and maintain confidentiality between different nodes on the IoT network [10], [18]- [25].…”

Section: Introductionmentioning

confidence: 99%

DITrust Chain: Towards Blockchain-Based Trust Models for Sustainable Healthcare IoT Systems

et al. 2020

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Today, internet and device ubiquity are paramount in individual, formal and societal considerations. Next generation communication technologies, such as Blockchains (BC), Internet of Things (IoT), cloud computing, etc. offer limitless capabilities for different applications and scenarios including industries, cities, healthcare systems, etc. Sustainable integration of healthcare nodes (i.e. devices, users, providers, etc.) resulting in healthcare IoT (or simply IoHT) provides a platform for efficient service delivery for the benefit of care givers (doctors, nurses, etc.) and patients. Whereas confidentiality, accessibility and reliability of medical data are accorded high premium in IoHT, semantic gaps and lack of appropriate assets or properties remain impediments to reliable information exchange in federated trust management frameworks. Consequently, We propose a Blockchain Decentralised Interoperable Trust framework (DIT) for IoT zones where a smart contract guarantees authentication of budgets and Indirect Trust Inference System (ITIS) reduces semantic gaps and enhances trustworthy factor (TF) estimation via the network nodes and edges. Our DIT IoHT makes use of a private Blockchain ripple chain to establish trustworthy communication by validating nodes based on their inter-operable structure so that controlled communication required to solve fusion and integration issues are facilitated via different zones of the IoHT infrastructure. Further, C implementation using Ethereum and ripple Blockchain are introduced as frameworks to associate and aggregate requests over trusted zones.

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“…The blockchain [2] consisting of a set of blocks and transactions is a distributed database that eliminates the need for a central authority. Blockchain has become an important component of the fundamental elements of in Industrial Internet of Things (IIOT) [3], Vehicle-to-everything (V2X) [4], and IoT because of its security, decentralization, and autonomy.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Graph Convolutional-LSTM for Energy-Efficient Caching in Blockchain-Based Green IoT

Chen

Yang

et al. 2021

IEEE Trans. on Green Commun. Netw.

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Nowadays, adopting blockchain technology to Internet of Things has become a trend and it is important to minimize energy consumption while providing a high quality of service (QoS) in Blockchain-based IoT networks. Pre-caching popular and fresh IoT content avoids activating sensors frequently, thus effectively reducing network energy consumption. However, the user equipment in regions covered by base stations will generate distributed and time-varying data requests, hence modeling the base station topology to capturing spatio-temporal request patterns is required for the data storage pre-allocation. Traditional solutions typically fail to pay attention to the topology, resulting in the sensor being activated redundantly. In this paper, we propose Request Graph Convolutional-LSTM to capture the spatio-temporal request patterns in Blockchain-based IoT networks and make predictions. Moreover, a heuristic algorithm based on the predictions is proposed to develop pre-caching strategy, which determines the data and location to be cached to minimize the mean data retrieval latency restricted by the cache space of IoT network entities and the freshness of IoT content. Experiments show that our proposed frame provides a low energy consumption.

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Collaborative Trust Blockchain Based Unbiased Control Transfer Mechanism for Industrial Automation

Cited by 43 publications

References 25 publications

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

DITrust Chain: Towards Blockchain-Based Trust Models for Sustainable Healthcare IoT Systems

Leveraging Graph Convolutional-LSTM for Energy-Efficient Caching in Blockchain-Based Green IoT

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