IEEE Open Journal of the Computer Society最新文献_第8页

Performance Analysis of Gossip Algorithms for Large Scale Wireless Sensor Networks 大规模无线传感器网络流言算法的性能分析

IEEE Open Journal of the Computer Society Pub Date : 2024-03-07 DOI: 10.1109/OJCS.2024.3397345

Sateeshkrishna Dhuli;Fouzul Atik;Anamika Chhabra;Prem Singh;Linga Reddy Cenkeramaddi

{"title":"Performance Analysis of Gossip Algorithms for Large Scale Wireless Sensor Networks","authors":"Sateeshkrishna Dhuli;Fouzul Atik;Anamika Chhabra;Prem Singh;Linga Reddy Cenkeramaddi","doi":"10.1109/OJCS.2024.3397345","DOIUrl":"10.1109/OJCS.2024.3397345","url":null,"abstract":"Gossip algorithms are often considered suitable for wireless sensor networks (WSNs) because of their simplicity, fault tolerance, and adaptability to network changes. They are based on the idea of distributed information dissemination, where each node in the network periodically sends its information to randomly selected neighbors, leading to a rapid spread of information throughout the network. This approach helps reduce the communication overhead and ensures robustness against node failures. They have been commonly employed in WSNs owing to their low communication overheads and scalability. The time required for every node in the network to converge to the average of its initial value is called the average time. The average time is defined in terms of the second-largest eigenvalue of a stochastic matrix. Thus, estimating and analyzing the average time required for large-scale WSNs is computationally complex. This study derives explicit expressions of average time for WSNs and studies the effect of various network parameters such as communication link failures, topology changes, long-range links, network dimension, node transmission range, and network size. Our theoretical expressions substantially reduced the computational complexity of computing the average time to \u0000<inline-formula><tex-math>$Oleft(n^{-3}right)$</tex-math></inline-formula>\u0000. Furthermore, numerical results reveal that the long-range links and node transmission range of WSNs can significantly reduce average time, energy consumption, and absolute error for gossip algorithms.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"290-302"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10521818","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140928391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generic Quantum Blockchain-Envisioned Security Framework for IoT Environment: Architecture, Security Benefits and Future Research 面向物联网环境的通用量子区块链安全框架：架构、安全优势和未来研究

IEEE Open Journal of the Computer Society Pub Date : 2024-03-07 DOI: 10.1109/OJCS.2024.3397307

Mohammad Wazid;Ashok Kumar Das;Youngho Park

{"title":"Generic Quantum Blockchain-Envisioned Security Framework for IoT Environment: Architecture, Security Benefits and Future Research","authors":"Mohammad Wazid;Ashok Kumar Das;Youngho Park","doi":"10.1109/OJCS.2024.3397307","DOIUrl":"10.1109/OJCS.2024.3397307","url":null,"abstract":"Quantum cryptography has the potential to secure the infrastructures that are vulnerable to various attacks, like classical attacks, including quantum-related attacks. Therefore, quantum cryptography seems to be a promising technology for the future secure online infrastructures and applications, like blockchain-based frameworks. In this article, we propose a generic quantum blockchain-envisioned security framework for an Internet of Things (IoT) environment. We then discuss some potential applications of the proposed framework. We also highlight the security advantages of quantum cryptography-based systems. We explain the working of blockchain, applications of blockchain, types of blockchain, the structure of blockchain, the structure of blockchain in a classical blockchain, and the structure of a block in a quantum blockchain context. Next, the adverse effects of quantum computing on the security of blockchain-based frameworks are highlighted. Furthermore, the comparisons of quantum cryptography-based security schemes, like quantum key distribution, quantum digital signature, and quantum hashing schemes, are provided. Finally, some future research directions related to the designed generic quantum blockchain-envisioned security framework for IoT are provided.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"248-267"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10521804","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140927997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on the use of Federated Learning in Privacy-Preserving Recommender Systems 关于在隐私保护推荐系统中使用联合学习的调查

IEEE Open Journal of the Computer Society Pub Date : 2024-03-02 DOI: 10.1109/OJCS.2024.3396344

Christos Chronis;Iraklis Varlamis;Yassine Himeur;Aya N. Sayed;Tamim M. AL-Hasan;Armstrong Nhlabatsi;Faycal Bensaali;George Dimitrakopoulos

{"title":"A Survey on the use of Federated Learning in Privacy-Preserving Recommender Systems","authors":"Christos Chronis;Iraklis Varlamis;Yassine Himeur;Aya N. Sayed;Tamim M. AL-Hasan;Armstrong Nhlabatsi;Faycal Bensaali;George Dimitrakopoulos","doi":"10.1109/OJCS.2024.3396344","DOIUrl":"10.1109/OJCS.2024.3396344","url":null,"abstract":"In the age of information overload, recommender systems have emerged as essential tools, assisting users in decision-making processes by offering personalized suggestions. However, their effectiveness is contingent on the availability of large amounts of user data, raising significant privacy and security concerns. This review article presents an extended analysis of recommender systems, elucidating their importance and the growing apprehensions regarding privacy and data security. Federated Learning (FL), a privacy-preserving machine learning approach, is introduced as a potential solution to these challenges. Consequently, the potential benefits and implications of integrating FL with recommender systems are explored and an overview of FL, its types, and key components, are provided. Further, the privacy-preserving techniques inherent to FL are discussed, demonstrating how they contribute to secure recommender systems. By illustrating case studies and significant research contributions, the article showcases the practical feasibility and benefits of combining FL with recommender systems. Despite the promising benefits, challenges, and limitations exist in the practical deployment of FL in recommender systems. This review outlines these hurdles, bringing to light the security considerations crucial in this context and offering a balanced perspective. In conclusion, the article signifies the potential of FL in transforming recommender systems, paving the path for future research directions in this intersection of technologies.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"227-247"},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10517657","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hand Gesture Recognition for Multi-Culture Sign Language Using Graph and General Deep Learning Network 利用图谱和通用深度学习网络识别多文化手语的手势

IEEE Open Journal of the Computer Society Pub Date : 2024-02-28 DOI: 10.1109/OJCS.2024.3370971

Abu Saleh Musa Miah;Md. Al Mehedi Hasan;Yoichi Tomioka;Jungpil Shin

{"title":"Hand Gesture Recognition for Multi-Culture Sign Language Using Graph and General Deep Learning Network","authors":"Abu Saleh Musa Miah;Md. Al Mehedi Hasan;Yoichi Tomioka;Jungpil Shin","doi":"10.1109/OJCS.2024.3370971","DOIUrl":"10.1109/OJCS.2024.3370971","url":null,"abstract":"Hand gesture-based Sign Language Recognition (SLR) serves as a crucial communication bridge between hard of hearing and non-deaf individuals. The absence of a universal sign language (SL) leads to diverse nationalities having various cultural SLs, such as Korean, American, and Japanese sign language. Existing SLR systems perform well for their cultural SL but may struggle with other or multi-cultural sign languages (McSL). To address these challenges, this paper introduces a novel end-to-end SLR system called GmTC, designed to translate McSL into equivalent text for enhanced understanding. Here, we employed a Graph and General deep-learning network as two stream modules to extract effective features. In the first stream, produce a graph-based feature by taking advantage of the superpixel values and the graph convolutional network (GCN), aiming to extract distance-based complex relationship features among the superpixel. In the second stream, we extracted long-range and short-range dependency features using attention-based contextual information that passes through multi-stage, multi-head self-attention (MHSA), and CNN modules. Combining these features generates final features that feed into the classification module. Extensive experiments with five culture SL datasets with high-performance accuracy compared to existing state-of-the-art models in individual domains affirming superiority and generalizability.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"144-155"},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10452793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140008733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model 评估区块链上的加密货币市场风险：使用 ARMA-GARCH-VaR 模型的实证研究

IEEE Open Journal of the Computer Society Pub Date : 2024-02-27 DOI: 10.1109/OJCS.2024.3370603

Yongrong Huang;Huiqing Wang;Zhide Chen;Chen Feng;Kexin Zhu;Xu Yang;Wencheng Yang

{"title":"Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model","authors":"Yongrong Huang;Huiqing Wang;Zhide Chen;Chen Feng;Kexin Zhu;Xu Yang;Wencheng Yang","doi":"10.1109/OJCS.2024.3370603","DOIUrl":"10.1109/OJCS.2024.3370603","url":null,"abstract":"Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)—over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"83-94"},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10449426","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling the Connection Between Malware and Pirated Software in Southeast Asian Countries: A Case Study 揭示东南亚国家恶意软件与盗版软件之间的联系：案例研究

IEEE Open Journal of the Computer Society Pub Date : 2024-02-09 DOI: 10.1109/OJCS.2024.3364576

Asif Iqbal;Muhammad Naveed Aman;Ramkumar Rejendran;Biplab Sikdar

{"title":"Unveiling the Connection Between Malware and Pirated Software in Southeast Asian Countries: A Case Study","authors":"Asif Iqbal;Muhammad Naveed Aman;Ramkumar Rejendran;Biplab Sikdar","doi":"10.1109/OJCS.2024.3364576","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3364576","url":null,"abstract":"Pirated software is an attractive choice for cybercriminals seeking to spread malicious software, known as malware. This paper attempts to quantify the occurrence of malware concealed within pirated software. We collected samples of pirated software from various sources from Southeast Asian countries, including hard disk drives, optical discs purchased in eight different countries, and online platforms using peer-to-peer services. Our dataset comprises a total of 750 pirated software samples. To analyze these samples, we employed seven distinct antivirus (AV) engines. The malware identified by the AV engines was classified into four categories: adware, Trojans, viruses, and a miscellaneous category termed others. Our findings reveal that adware and Trojans are the most prevalent types of malware, with average infection rates of 34% and 35%, respectively, among our pirated software samples. Notably, our evaluation of AV detection performance highlights variations in sensitivity, ranging from a high of 132% to a low of 30% across all AV engines. Furthermore, upon installing pirated software, the most adversely affected operating system settings are the firewall and user account control configurations. Given the potential for malware to steal information or create malicious backdoors, its high prevalence within pirated software poses a substantial security risk to end users.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"62-72"},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10430375","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139937097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CTLA: Compressed Table Look up Algorithm for Open Flow Switch CTLA：开式流量开关的压缩查表算法

IEEE Open Journal of the Computer Society Pub Date : 2024-02-02 DOI: 10.1109/OJCS.2024.3361710

Veeramani Sonai;Indira Bharathi;Muthaiah Uchimucthu;Sountharrajan S;Durga Prasad Bavirisetti

{"title":"CTLA: Compressed Table Look up Algorithm for Open Flow Switch","authors":"Veeramani Sonai;Indira Bharathi;Muthaiah Uchimucthu;Sountharrajan S;Durga Prasad Bavirisetti","doi":"10.1109/OJCS.2024.3361710","DOIUrl":"10.1109/OJCS.2024.3361710","url":null,"abstract":"The size of the TCAM memory grows as more entries are added to the flow table of Open Flow switch. The procedure of looking up an IP address involves finding the longest prefix. In order to keep up with the link speed, the IP lookup operation in the forwarding table should also need to be speed up. TCAM's scalability and storage are constrained by its high power consumption and circuit density. The only time- or space-efficient algorithms for improvement are the subject of several research studies. In order to boost performance even further, this study focuses on time and space efficient algorithms. To strike a balance between speedy data access and efficient storage, this study proposes a combination of compression and a quick look-up mechanism to satisfy the space and speed requirements of the Open Flow switch. As the data is compressed, performance improves because less memory is required to store the look-up table and fewer bits are required to search. The look up complexity of proposed approach is \u0000<inline-formula><tex-math>$O(log,(log;n/2))$</tex-math></inline-formula>\u0000 and average space reduction is 61%.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"73-82"},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10419009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139952219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Slingshot: Globally Favorable Local Updates for Federated Learning 弹弓：联盟学习的全局有利局部更新

IEEE Open Journal of the Computer Society Pub Date : 2024-01-22 DOI: 10.1109/OJCS.2024.3356599

Jialiang Liu;Huawei Huang;Chun Wang;Sicong Zhou;Ruixin Li;Zibin Zheng

{"title":"Slingshot: Globally Favorable Local Updates for Federated Learning","authors":"Jialiang Liu;Huawei Huang;Chun Wang;Sicong Zhou;Ruixin Li;Zibin Zheng","doi":"10.1109/OJCS.2024.3356599","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3356599","url":null,"abstract":"Federated Learning (FL), as a promising distributed learning paradigm, is proposed to solve the contradiction between the data hunger of modern machine learning and the increasingly stringent need for data privacy. However, clients naturally present different distributions of their local data and inconsistent local optima, which leads to poor model performance of FL. Many previous methods focus on mitigating objective inconsistency. Although local objective consistency can be guaranteed when the number of communication rounds is infinite, we should notice that the accumulation of global drift and the limitation on the potential of local updates are non-negligible in those previous methods. In this article, we study a new framework for data-heterogeneity FL, in which the local updates in clients towards the global optimum can accelerate FL. We propose a new approach called \u0000<italic>Slingshot</i>\u0000. Slingshot's design goals are twofold, i.e., i) to retain the potential of local updates, and ii) to combine local and global trends. Experimental results show that \u0000<italic>Slingshot</i>\u0000 helps local updates become more globally favorable and outperforms other popular methods under various FL settings. For example, on CIFAR10, \u0000<italic>Slingshot</i>\u0000 achieves 46.52% improvement in test accuracy and 48.21× speedup for a lightweight neural network named \u0000<italic>SqueezeNet</i>\u0000.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"39-49"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10411043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low Area and Low Power FPGA Implementation of a DBSCAN-Based RF Modulation Classifier 基于 DBSCAN 的射频调制分类器的低面积、低功耗 FPGA 实现

IEEE Open Journal of the Computer Society Pub Date : 2024-01-18 DOI: 10.1109/OJCS.2024.3355693

Bill Gavin;Tiantai Deng;Edward Ball

引用次数: 0

IEEE Open Journal of the Computer Society Information for Authors IEEE 计算机学会公开期刊作者须知

IEEE Open Journal of the Computer Society Pub Date : 2023-12-15 DOI: 10.1109/OJCS.2023.3239731

引用次数: 0