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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2056-2069页

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Web applications testing techniques: a systematic mapping study

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International Journal of Web engineering and Technology 2023年第4期17卷 372-412页

作者： Hanna, Samer Ahmad, Amro Al-Said Department of Software Engineering Faculty of Information Technology Philadelphia University Jordan School of Computing and Mathematics University of Keele United Kingdom

Due to the importance of web application testing techniques for detecting faults and assessing quality attributes, many research papers were published in this field. For this reason, it became essential to analyse, classify and summarise the research in the field. To achieve this goal, this research conducted a systematic mapping study on 98 research papers in the field of web applications testing published between 2008 and 2021. The results showed that the most commonly used web applications testing techniques in literature are model-based testing and security testing. Besides, the most commonly used models in model-based testing are finite-state machines. The most targeted vulnerability in security testing is SQL injection. Test automation is the most targeted testing goal in both model-based and security testing. For other web applications testing techniques, the main goals of testing were test automation, test coverage, and assessing security quality attributes. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Mapping

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Design of a Secured and Trusted E-Voting System Using Blockchain Technology

Design of a Secured and Trusted E-Voting System Using Blockc...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Elegba Jeremiah, O. Agbelusi, O. Olutayo Victor, A. Akintola Kolawole, G. School of Computing The Federal University of Technology Department of Software Engineering Akure Nigeria School of Computing The Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9798350376838

The state of election in Nigeria is worrisome. Electoral malpractices have been a major challenge to the Nigerian government in recent times. Government has made frantic efforts to tackle these electoral challenges but the recent event in year 2023 presidential election is worrisome suggests that the solution is not near. Recently, the percentage of eligible voters who vote on election days is declining. Voters turnout went up from 52.3% in 1999 - the first general election since 1993 - to 69% in 2003. But, it has been on the decline nearly ever since - 57.5% in 2007, 53.7% in 2011, 43.7% in 2015 and 34.8% in 2019. In 2023, it is 28.63%. With the way things are going, if urgent action is not taken, this could signal the end of democracy in Nigeria. This research presents the design and implementation of a trusted and secured e-voting based on blockchain technology that will increase voters confidence and trust. The system utilizes *** for the frontend and Solidity on the Polygon blockchain for the backend, ensuring transparency, tamper-resistance, and heightened security. This innovative approach addresses trust and reliability concerns in traditional voting systems, offering a modernized and trustworthy electoral process. © 2024 IEEE.

关键词： Blockchain

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Integrating Private and Accountable Threshold Signature Into Ciphertext-Policy Attribute-Based Encryption Supporting Collaboration Decryption

Integrating Private and Accountable Threshold Signature Into...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Meixin School of Software Engineering East China Normal University Shanghai Key Laboratory of Trustworthy Computing China

ISBN: (纸本)9781665410205

Cloud services are becoming increasingly popular in social life. Collaboration in the cloud is critical for teams in different locations and organizations. However, the current collaborative decryption scheme requires all members to be online in order to decrypt, which causes offline members to affect the progress of the project. In this paper, we propose a more flexible and reliable collaborative decryption solution for team collaboration scenarios in cloud services. Our solution, based on the Threshold Accountable Private Signature (TAPS) framework, offers higher security, practicality, and lower overhead. Unlike the existing scheme, our approach allows for decryption when the number of online members reaches a certain threshold. Additionally, our framework can track individual members instead of all members, ensuring continuity of collaboration. To demonstrate the practicality and efficiency of our framework, we implement and verify its usefulness through experiments using the Rust programming language. © 2024 IEEE.

关键词： Ciphertext

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Enhanced Detection of Glass Insulator Defects Using Improved Generative Modeling and Faster RCNN 18

Enhanced Detection of Glass Insulator Defects Using Improved...

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18th CIRP Conference on Computer Aided Tolerancing, CAT 2024

作者： Ning, Pin Jin, Jin Xu, Yuanping Kong, Chao Zhang, Chaolong Tang, Dan Huang, Jian Xu, Zhijie Li, Tukun School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom

The precise defect detections for glass insulators are of utmost importance to ensure their safety and functionality. Therefore, this study proposes an improved defect detection algorithm based on the optimized deep learning network. The collected glass insulator data was limited in size. Even after data augmentation, it remained insufficient for the training requirements of deep learning models. In this study, employing the improved Denoising Diffusion Probabilistic Models (DDPM) generative model to expand the glass insulator data. The glass insulator defect images generated by the improved noise-fitting network exhibit enhanced quality and high fidelity. Through manual selection and iterative experiments, a total of 1200 images were curated, constituting the glass insulator defect dataset for training and test of deep learning models. Faster RCNN was chosen as the defect detection model, and its VGG16 feature extraction network was replaced with ResNet50 to address the issue of gradient vanishing caused by excessive network stacking. Additionally, the Feature Pyramid Network (FPN) structure was introduced to enhance semantic extraction for different defect scales. The Kmeans++ algorithm was utilized to improve the proposal box generation parameters in RPN. Compared to the baseline Faster RCNN model's mAP of 72.7%, our improved version achieved a significant increase, reaching a mAP of 85.9%. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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An Improved PointNet++ Based Method for 3D Point Cloud Geometric Features Segmentation in Mechanical Parts 18

An Improved PointNet++ Based Method for 3D Point Cloud Geome...

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18th CIRP Conference on Computer Aided Tolerancing, CAT 2024

作者： Zhang, Peng Kong, Chao Xu, Yuanping Zhang, Chaolong Jin, Jin Li, Tukun Jiang, Xiangqian Tang, Dan School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom

The extraction of geometric features such as holes, arcs, and surfaces of mechanical parts is crucial for quality control. The existing methods for geometrical feature segmentations on 3D point clouds still have limitations, especially for simultaneously extracting multiple types of geometric features from comprehensive workpieces. To this end, this study investigates segmentation methods that take 3D point cloud datasets of mechanical parts as inputs, and employs an improved PointNet++ deep learning model to solve this extraction difficulty. Firstly, the Set Abstraction module in PointNet++ was modified by incorporating Self-Attention mechanisms to increase interactivity and global correlation among data points. Then, the local feature extraction Multilayer Perceptron (MLP) from PointNet-Transformer was integrated to enhance the feature extraction accuracy. Due to the inherent class imbalance issue, the Focal Tversky Loss is employed as the loss function to ensure that geometric features with relatively lower proportions can be fully trained. Finally, the Statistical filtering algorithm is utilized to mitigate noise and attenuate subtle irregularities, such that the smoothness of geometric features can be substantially enhanced. The experimental results demonstrate that the proposed model achieves an accuracy of 86.6% on geometric feature segmentations and a mean Intersection over Union (mIoU) of 0.84. The comparison with the original PointNet++ proves that the proposed method can improve accuracy and mIoU by 3.7% and 0.03 respectively. © 2024 The Authors. Published by Elsevier B.V.

关键词： Geometry

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Huffman Tree Compression and Lempel-Ziv Coding Using Java

Huffman Tree Compression and Lempel-Ziv Coding Using Java

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Nuha, Hilal H. Rizky Putra Sailellah, Hassan Abo Absa, Ahmed School of Computing Telkom University Bandung Indonesia University of Palestine Software Engineering Gaza Strip Palestine

ISBN: (纸本)9798350369106

Lossless data compression is necessary to reduce transmission costs while maintaining data integrity. This paper describes Kompressor as a Huffman and Lempel Ziv (LZ) compression algorithm project using Java. All major Java class implementations are discussed in detail. The algorithm is evaluated for three different files to measure performance such as compression ratio, code rate, and efficiency. The program is shown to be capable of performing compression and decompression as proposed in the original algorithm for different parameter values. Huffman encoding, in particular, is capable of shrinking files to less than 70% of their original file size. It is hoped that this project will be useful for academic and scientific purposes, especially for courses based on data compression. © 2024 IEEE.

关键词： Java programming language

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Redactable Blockchain From Decentralized Chameleon Hash Functions, Revisited

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IEEE Transactions on Computers 2025年第6期74卷 1911-1920页

作者： Li, Cong Shen, Qingni Wu, Zhonghai Peking University School of Software and Microelectronics Beijing100871 China Peking University National Engineering Research Center for Software Engineering Beijing100871 China Peking University Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing100871 China Peking University PKU-OCTA Laboratory for Blockchain and Privacy Computing Beijing100871 China

Recently, redactable blockchains have attracted attention owing to enabling the contents of blocks to be re-written. The existing redactable blockchain solutions can be classified as two categories, the centralized one and decentralized one. In centralized solutions, a single blockchain node possessing the trapdoor conducts redaction operations. However, they suffer from the issue of single point of failure. In decentralized solutions, redaction operations are performed by numerous blockchain nodes cooperatively. But there also exists the issue of inefficiency or requiring a trusted party in these solutions. Subsequently, Jia et al. proposed a redactable blockchain solution from a decentralized chameleon hash function (DCH) they designed, which supports the threshold redaction, traceability and consistency check. Nevertheless, after carefully analyzing their solution, we find that it fails to achieve the security they claimed by presenting a concrete attack. To resolve this security issue, we propose a novel chameleon hash function scheme that achieves strong collision-resistant security while maintaining simple and efficient as the building block. Based on it, we then present an improved DCH scheme with sufficient security, so that the redactable blockchain from it can resist the presented attack. Theoretical and experimental analyses demonstrate that improved DCH achieves efficiency comparable to DCH. © 1968-2012 IEEE.

关键词： Decentralized control

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ALGIN: Adaptive Local and Global Interests Network for Click-Through Rate Prediction

ALGIN: Adaptive Local and Global Interests Network for Click...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Xiang, Yingjia Lu, Xuanyu Zhou, Wei Wen, Junhao School of Big Data and Software Engineering Chongqing University Chongqing China School of Computing China University of Geosciences Wuhan China

ISBN: (纸本)9781665410205

User behavior sequence modeling represents users' interests and preferences, which has a crucial impact on click-through rate (CTR) prediction models and recommendation systems. In practical scenarios, multiple types of interactive behaviors usually constitute users' complex interests and preferences. However, existing CTR prediction models based on multi-type interactive behavior modeling have two limitations: (1) they cannot handle noise in users' global interests, resulting in the inability to model accurate global interest preferences;(2) they struggle to depict users' local interest patterns and cannot model the complex patterns embedded in multi-behavior sequences. To overcome these limitations, an Adaptive Local and Global Interests Network For Click-Through Rate Prediction (ALGIN) is proposed, which effectively models global and local interests within multi-behavior interaction sequences through a unified framework. Firstly, ALGIN employs a Fourier Global Interest Modeling module to eliminate noise in the behavioral sequence;secondly, ALGIN designs a Multi-Scale Local Interest Extraction module to capture behavioral patterns within multi-type interactive sequences;finally, an Item-driven Interest Selector is used to integrate users' local interests with their global interests. Experiments on multiple public datasets demonstrate that the ALGIN model outperforms the current state-of-the-art (SOTA), and ablation experiments confirm the effectiveness of each module within the model. © 2024 IEEE.

关键词： Prediction models

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