A Blockchain Based Cloud Integrated IoT Architecture Using a Hybrid Design

Rupa, Ch.; Srivastava, Gautam; Gadekallu, Thippa Reddy; Maddikunta, Praveen Kumar Reddy; Bhattacharya, Sweta

doi:10.1007/978-3-030-67540-0_36

Cited by 14 publications

(5 citation statements)

References 45 publications

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“…Likewise, many of the security measures currently utilized to protect that data cannot always do so adequately. Subsequently, the majority of third-party services currently offered still come with vulnerabilities that could leave users' sensitive private information open to theft and unauthorized access or exposure [37][38][39][40][41][42]. us, in order to directly enhance the approaches to security and privacy concerns regarding users' personal information, this work presents an approach in which such information is enciphered through a novel enhanced and dynamic nonpolynomial integrity centric quantum HCP-ABE prototype to store big cloud datasets prior to storage.…”

Section: Proposed Modelmentioning

confidence: 99%

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Singamaneni

Juneja²,

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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Topics such as computational sources and cloud-based transmission and security of big data have turned out to be a major new domain of exploration due to the exponential evolution of cloud-based data and grid facilities. Various categories of cloud services have been utilized more and more widely across a variety of fields like military, army systems, medical databases, and more, in order to manage data storage and resource calculations. Attribute-based encipherment (ABE) is one of the more efficient algorithms that leads to better consignment and safety of information located within such cloud-based storage amenities. Many outmoded ABE practices are useful for smaller datasets to produce fixed-size cryptograms with restricted computational properties, in which their characteristics are measured as evidence and stagnant standards used to generate the key, encipherment, and decipherment means alike. To surmount the existing problems with such limited methods, in this work, a dynamic nonlinear poly randomized quantum hash system is applied to enhance the safety of cloud-based information. In the proposed work, users’ attributes are guaranteed with the help of a dynamic nonlinear poly randomized equation to initialize the chaotic key, encipherment, and decipherment. In this standard, structured and unstructured big data from clinical datasets are utilized as inputs. Real-time simulated outcomes demonstrate that the stated standard has superior exactness, achieving over 90% accuracy with respect to bit change and over 95% accuracy with respect to dynamic key generation, encipherment time, and decipherment time compared to existing models from the field and literature. Experimental results are demonstrated that the proposed cloud security standard has a good efficiency in terms of key generation, encoding, and decoding process than the conventional methods in a cloud computing environment.

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Section: Proposed Modelmentioning

confidence: 99%

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Singamaneni

Juneja²,

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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show abstract

“…The current study's analysis goal is to analyze the risk variables and the correlations that exist among these risk factors.For prediction, Naive Bayes, SVM, random forest, logistic regression, decision tree and KNN classifiers are employed. Rupa et al [35] A blockchain-based cloud-integrated IoT solution is offered, which can aid in the detection of intruders via virtual surveillance. The key feature of this technique is that it may work in regions where monitoring and control is difficult, and data is saved in a tamper-proof blockchain environment…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud-based bug tracking software defects analysis using deep learning

Tao

Zhou

et al. 2022

J Cloud Comp

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Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. This issue is even cumbersome in the embedded software design. Early detection of defect-prone components in general and embedded software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing for both embedded and general-purpose software while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Not withstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.

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“…In current vehicle security applications, traditional machine learning‐based intrusion detection systems have drawbacks (Baza et al, 2020; Kamal et al, 2020; Rupa et al, 2020). Sensors must broadcast large volumes of data to a distant server, requiring more network encoding and transmission time.…”

Section: Introductionmentioning

confidence: 99%

Blockchain‐based multi‐layered federated extreme learning networks in connected vehicles

et al. 2022

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Intelligent and networked vehicles help build an efficient vehicular network's infrastructure. The widespread use of electronic software exposes these networks to cyber‐attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real‐time performance. Current learning‐based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain‐based Multi‐Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF‐IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi‐Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road‐Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real‐time data sets, the suggested algorithm's performance under different attack scenarios were extensively tested. The suggested method obtained 98% accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it's incredibly secure and costs very little to transmit.

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A Blockchain Based Cloud Integrated IoT Architecture Using a Hybrid Design

Cited by 14 publications

References 45 publications

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Cloud-based bug tracking software defects analysis using deep learning

Blockchain‐based multi‐layered federated extreme learning networks in connected vehicles

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