Hashed Needham Schroeder Industrial IoT based Cost Optimized Deep Secured data transmission in cloud

Alzubi, Jafar A.; Ramachandran, Manikandan; Alzubi, Omar A.; Qiqieh, Issa; Rahim, Robbi; Gupta, Deepak; Khanna, Ashish

doi:10.1016/j.measurement.2019.107077

Cited by 59 publications

(20 citation statements)

References 20 publications

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“…Public Key Generation (PKG) mechanism is used to measure the computational cost and overhead. Data transfers are implemented using the cloud by any user with authenticated privileges and join the receivers with asymmetric cryptography techniques [57].…”

Section: B Deep Learning-based Solutionsmentioning

confidence: 99%

A Taxonomy of Security Issues in Industrial Internet-of-Things: Scoping Review for Existing Solutions, Future Implications, and Research Challenges

et al. 2021

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Industrial Internet of Things (IIoTs) are the extensions of the Internet of Things (IoTs) and have paved the way towards the industry revolution 4.0. IIoT accelerates the industry automation of internal and external working process including transport, manufacturing, and marketing units with a number of connected devices. Being the extension of IoT, it inherits the insecurities of the technology; however, this sensor-configured infrastructure of IIoT needs some extra effort to customize the existing security solutions. In spite of the reconstructions of security models, the scope of improved developments is open to detect unknown attacks. The present study helps the researchers to understand the cause for intrusion by classifying and comparing various attacks of each IIoT layers. The main focus of this survey is to analyze various security issues faced by IIoT and provide a comparative analysis on the available solutions to enhance the industrial IoTs' protection systems. This study also notifies some open research problems for academia, technologists, and researchers to flourish the IIoT domain and its security aspects.

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Section: B Deep Learning-based Solutionsmentioning

confidence: 99%

A Taxonomy of Security Issues in Industrial Internet-of-Things: Scoping Review for Existing Solutions, Future Implications, and Research Challenges

et al. 2021

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“…The authors have performed a comparative analysis with existing models and found to be superior [20]. Jafar A. Alzubi et al [21] have devised a scheme to provide security for IIoT data transfer using cloud services with a Hashed Needham Schroeder (HNS) cost-optimized deep machine Learning (CODML) technique that shows the need to deliver IIoT security. A public key using HNS is computed that provides access to the device.…”

Section: Related Workmentioning

confidence: 99%

A Secure Industrial Internet of Things (IIoT) Framework for Resource Management in Smart Manufacturing

Abuhasel

Khan

2020

IEEE Access

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The Industrial Internet of Things (IIoT) can transform an existing isolated industrial system to a connected network. The IIoT and the related wireless connectivity requirements for industrial sensors are very significant. The deployed sensors in IIoT monitors the conditions of the industrial devices and machines. Therefore, reliability and security become the most important concerns in IIoT. This introduces many familiar and ever-increasing risks associated with the industrial system. The IIoT devices can be vulnerable to vast array of viruses, threats, and attacks. Therefore, an efficient protection strategy is required to ensure that the millions of IIoT devices are safe from these risks. However, resource constraint IIoT devices have not been designed to have effective security features. Due to this, in recent years, cloud, fog, and edge-based IIoT has received great attention in the research community. The computationally intensive tasks such as security, data analytics, decision making, and reporting are performed at the cloud or fog using a powerful computing infrastructure. The data security of the IIoT device has been provided by employing improved Rivest-Shamir-Adelman (RSA) and hash signatures. The proposed RSA algorithm has a four-prime number of 512-bits. The device authentication is performed by employing a hash signature. For long network life, an efficient clustering technique for the sensor devices which is based on node degree(N), distance from the cluster(D), residual energy(R), and fitness (NDRF) has been proposed. The fitness of the sensor nodes is computed using the Salp swarm algorithm (SSA). In order to reduce the latency and communication overhead for IIoT devices a resource scheduling using SoftMax deep neural network (DNN) is proposed. All the requests coming from the cluster head are classified using SoftMax-DNN for best resource scheduling on the basis of storage, computing, and bandwidth requirements. The proposed framework produces superior results, especially in terms of energy consumption, latency, and strength of security.

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“…In recent times, 3 kinds of COVID-19 testing methods are named as Reverse Transcription Polymerase Chain Reaction (RT-PCR), CT Scan, and Chest X-Ray (CXR). Additionally, DL is evolved from Machine Learning (ML) which performs automated training or learning delicate features from the data [ 5 ]. In clinical imaging, massive DL models were applied with Convolutional Neural Network (CNN).…”

Section: Introductionmentioning

confidence: 99%

IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

Velmurugan

Gupta

et al. 2021

Int. J. Mach. Learn. & Cyber.

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104

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Hashed Needham Schroeder Industrial IoT based Cost Optimized Deep Secured data transmission in cloud

Cited by 59 publications

References 20 publications

A Taxonomy of Security Issues in Industrial Internet-of-Things: Scoping Review for Existing Solutions, Future Implications, and Research Challenges

A Taxonomy of Security Issues in Industrial Internet-of-Things: Scoping Review for Existing Solutions, Future Implications, and Research Challenges

A Secure Industrial Internet of Things (IIoT) Framework for Resource Management in Smart Manufacturing

IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

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