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A Distributed Self-Tallying Electronic Voting System Using the Smart Contract

Chinese Journal of Electronics 2024年第4期33卷 1063-1076页

作者： Jingyu YAO Bo YANG Tao WANG Wenzheng ZHANG School of Computer Science Shaanxi Normal University State Key Laboratory of Integrated Services Networks Xidian University Science and Technology on Communication Security Laboratory

For electronic voting(e-voting) with a trusted authority, the ballots may be discarded or tampered, so it is attractive to eliminate the dependence on the trusted party. An e-voting protocol, where the final voting result can be calculated by any entity, is known as self-tallying e-voting protocol. To the best of our knowledge, addressing both abortive issue and adaptive issue simultaneously is still an open problem in self-tallying e-voting *** Ethereum blockchain with cryptographic technologies, we present a decentralized self-tallying e-voting protocol. We solve the above problem efficiently by utilizing optimized Group Encryption Scheme and standard Exponential El Gamal Cryptosystem. We use zero-knowledge proof and homomorphic encryption to protect votes' secrecy and achieve self-tallying. All ballots can be verified by anyone and the final voting result can be calculated by any entity. In addition, using the paradigm of score voting and “1-out-of-k” proof, our e-voting system is suitable for a wide range of application scenarios. Experiments show that our protocol is more competitive and more suitable for large-scale voting.

关键词： Electronic voting systems Smart contracts Blockchains Cryptocurrency Electronic voting Homomorphic encryption Standards

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Decomposition-based learning in drone-assisted wireless-powered mobile edge computing networks

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Digital communications and networks 2024年第6期10卷 1769-1781页

作者： Xiaoyi Zhou Liang Huang Tong Ye Weiqiang Sun State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong UniversityShanghai 200240China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310058China

This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile *** aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all *** action space of the system is extremely large and grows exponentially with the number of *** this case,single-agent learning will require an overlarge neural network,resulting in insufficient ***,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for *** thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale *** the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this *** the second tier,UAVs utilize multiple agents to plan their *** agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint *** results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific *** particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal ***,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.

关键词： Mobile-edge computing Multi-agent reinforcement learning Offloading decision Trajectory planning Unmanned aerial vehicle Wireless power transfer

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Secure Resource Allocation Protocol (SecRAP) for Time-Sensitive Networking 29

Secure Resource Allocation Protocol (SecRAP) for Time-Sensit...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Osswald, Lukas Lindner, Steffen Bechtel, Lukas Heer, Tobias Menth, Michael University of Tuebingen Communication Networks Tuebingen72076 Germany Esslingen University Computer Science and Engineering Esslingen73732 Germany

ISBN: (纸本)9798350361230

The convergence of operational technology (OT) and information technology (IT) networks through Time-Sensitive Networking (TSN) promises enhanced efficiency and new use cases in industrial settings. However, ensuring security in this shared infrastructure is crucial to prevent potential attacks that could compromise Quality of Service (QoS) of real-time streams and pose risks to operations and safety. This paper focuses on auditing the security of the Resource Allocation Protocol (RAP), a distributed QoS signaling protocol for TSN. We analyze the vulnerability of RAP to attacks during admission control, where end stations request network resources for data transmission. We leverage the Dolev-Yao attacker model to assess the security properties of RAP in both distributed hop-by-hop admission control and hybrid admission control with a central controller. We introduce novel security extensions to RAP, called Secure Resource Allocation Protocol (SecRAP), to mitigate the identified attack vectors. Finally, we present a prototype and discuss the security properties of SecRAP. © 2024 IEEE.

关键词： Access control

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CIL4EMTD: A Novel Class Incremental Learning Method for Encrypted Malware Traffic Detection 9

CIL4EMTD: A Novel Class Incremental Learning Method for Encr...

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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Li, Jiarui Du, Lei Chen, Yilu Zeng, Liyi Li, Hao Gu, Zhaoquan School of Computer Science and Technology Shenzhen China Pengcheng Laboratory Department of New Networks Shenzhen China National Key Laboratory of Advanced Communication Networks Shijiazhuang China

ISBN: (纸本)9798350391367

With the widespread adoption of traffic encryption technologies, detecting attack behaviors in encrypted malware traffic has become a critical and challenging problem. The encrypted malware traffic contains a lot of redundant information and noise, which necessitates additional computational resources. In addition, new attack categories continue to emerge over time, resulting in traditional detection methods losing effectiveness in identifying these new attacks. To handle the above challenge, in this paper, we propose CIL4EMTD, a novel Class Incremental Learning method targeted for Encrypted Malware Traffic Detection. Our method leverages clustering techniques to enrich the extraction of dataset features, while specifically customizing LwF for detecting encrypted malware to optimize the stability of model training via efficient knowledge distillation. To verify the effectiveness of CIL4EMTD, we conduct experiments comparing it with other class incremental learning methods. The experiment results show that CIL4EMTD demonstrates state-of-the-art performance on a real-world dataset, achieving a recognition accuracy of 94.34% for new categories while maintaining the average accuracy for known categories within a margin of less than a 10% decline. © 2024 IEEE.

关键词： Adversarial machine learning

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5G Channel Estimation Kernels on RISC-V Vector Digital Signal Processors

5G Channel Estimation Kernels on RISC-V Vector Digital Signa...

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2024 International Conference on Microelectronics, ICM 2024

作者： Acevedo, Javier Fitzek, Frank H. P. Seeling, Patrick Deutsche Telekom Chair of Communication Networks TU Dresden Germany Germany Central Michigan University Department of Computer Science United States

ISBN: (纸本)9798350379396

Each generation of the wireless communication networks has contiguous demands for higher computing capabilities and lower the power consumption in the integrated circuitry. Thus, the optimization of the base-band signal processing kernels and the hardware architectures plays a pivotal role in the performance, latency, and energy efficiency of the Physical Layer (PHY). Channel Estimation (CE), a computationally intensive task with hard real-time requirements, involves significant amounts of matrix multiplications and inversions, when data flows between transmitter (TX) and receiver (RX). In this work, we present a software implementation of CE for the 5th Generation Cellular networks (5G) New Radio (NR) in the Reduced Instruction Set computer (RISC)-V architecture, showcasing how its vector processing capabilities can satisfy the throughput and delay requirements of 5G use cases. Specifically, we ported two CE kernels, the Least-Squares (LSE) and Minimum Mean Square Error (MMSE), onto a state-of-the-art single core vector processor. By exploiting data vectorization, the ARA core computes matrix arithmetic operations on multiple data sets simultaneously. Our single vector core serial optimization demonstrates that with a 16× 16 matrix window size and 16 vector lanes, the application's performance achieves a speedup of 78.27 when computing the LSE algorithm. When the number of operations per cycle are compared, the vector processor outperforms its scalar counterpart by 181.91 for the MMSE kernel. Our implementation orders the nested loops in a latency-reducing fashion to compute matrix arithmetic operations, resulting in LSE and MMSE estimator computing times of 8.44 μiota s and 12.31 μs, respectively. © 2024 IEEE.

关键词： Mean square error

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Combined QoS Analysis of Smart Distribution Grids with Constraint Satisfaction Problem and Network Calculus 14

Combined QoS Analysis of Smart Distribution Grids with Const...

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14th IEEE International Conference on communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Alshra'a, Abdullah S. Vietz, Matthias Hielscher, Kai-Steffen Oest, Frauke Lehnhoff, Sebastian German, Reinhard Friedrich-Alexander-University Computers Networks and Communication Systems Germany University of Oldenburg Department of Computer Science Germany

ISBN: (纸本)9781665455541

The expansion of renewable energies requires implementing Smart Grid Services (SGSs) in the distribution grid. Analytical verification of the SGSs' Quality of Service (QoS) requirements is crucial for reliable operation. This paper proposes a two-step approach: a constraint satisfaction problem (CSP) considers network infrastructure and resource allocations to find candidates' solutions. Then, the solutions are evaluated using Network Calculus (NC) to calculate delay bounds at the worst-case and compare them to the requirements. If necessary, alternative solutions are generated. The NC framework is adjustable and considers different service priorities by implementing scheduling policies such as strict priority, weighted fair queuing, weighted round robin, and deficit round robin. In the end, the best-performing parameters are derived for a sample network and the results manifest that the NC framework can be used to analyze random communication networks. © 2023 IEEE.

关键词： Quality of service

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network Data

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SN computer science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The Degree of Correlation Network Model (DCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based Deep Analyzing Network (SA-DAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification Degree of correlation network model (DCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-DAN) Semantic analysis Social data

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Machine Learning Based Study of QoE Metrics in *** Live Streaming 36

Machine Learning Based Study of QoE Metrics in *** Live Stre...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Loh, Frank Hildebrand, Kathrin Wamser, Florian Geißler, Stefan Hoßfeld, Tobias University of Würzburg Communication Networks Germany Lucerne University of Applied Sciences and Arts School of Computer Science and Information Technology Switzerland

ISBN: (纸本)9781665477161

Video streaming generates most network traffic in today's Internet. For that reason, video on demand streaming is researched heavily in recent years, and many traffic monitoring mechanisms, flow and stream models, and models to predict the user perceived quality are well established. However, the quickly growing live streaming sector is not considered in these Quality of Experience models and not even the relation between network traffic and playback quality has been studied so far, forming a gap in literature. For that reason, we investigate *** streaming as one of the largest live streaming platforms based on a large dataset and investigate the possibility to predict live streaming quality based on uplink request information. We apply approaches that are well studied for on demand streaming to predict quality changes, playback quality, and video interruption events as the most important metrics impairing user perceived quality. In this context, we answer whether these models are suitable for live streaming, if small changes are sufficient for satisfactory prediction results, or if fundamental changes and new models are required. © 2023 IEEE.

关键词： Machine learning

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Research on Optimization and Scheduling of MPTCP Data networks in GNSS Network Reference Stations

Research on Optimization and Scheduling of MPTCP Data Networ...

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

作者： Hao, Fengqi Wang, Yuxin Kong, Hoiio Ma, Dexin Zhao, Xiyuan Jinan China City University of Macau Faculty of Data Science China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Communication College Qingdao Agricultural University Qingdao266109 China

ISBN: (纸本)9781665410205

The Global Navigation Satellite System Network Reference Stations (GNSS-NRS) are pivotal in modern po-sitioning and navigation applications. However, GNSS-NRS face significant communication challenges, including instability during rapid user movement and inconsistent coverage by different operators, which often results in data transmission interruptions and delays. Moreover, the conventional Multipath Transmission Control Protocol (MPTCP) scheduling strategy does not accommodate the diversity of the data types managed by GNSS-NRS. This default approach treats all data uniformly, even under deteriorating network conditions. To overcome these limitations, this paper introduces a 'Multiple Reception' strategy based on MPTCP that ensures timely and reliable GNSS data transmission. This novel strategy differentiates between observational and control data based on their real-time criticality and applies tailored transmission techniques. Under adverse conditions, it utilizes multiple transmission paths and selectively receives control data, thus prioritizing the transfer of critical information. We conducted simulation experiments using MiniNet to compare the 'Multiple Reception' method against conventional and redundant strategies, demonstrating that it significantly reduces data loss rates in GNSS-NRS. © 2024 IEEE.

关键词： Global positioning system

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