2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)最新文献

Distance-Statistical Based Byzantine-Robust Algorithms in Federated Learning 联合学习中基于距离统计的拜占庭稳健算法

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454840

Francesco Colosimo, F. Rango

{"title":"Distance-Statistical Based Byzantine-Robust Algorithms in Federated Learning","authors":"Francesco Colosimo, F. Rango","doi":"10.1109/CCNC51664.2024.10454840","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454840","url":null,"abstract":"New machine learning (ML) paradigms are being researched thanks to the current widespread adoption of AI-based services. Since it enables several users to cooperatively train a global model without disclosing their local training data, Federated Learning (FL) represents a new distributed methodology capable of attaining stronger privacy and security guarantees than current methodologies. In this paper, a study of the properties of FL is conducted, with an emphasis on security issues. In detail, a thorough investigation of currently known vulnerabilities and their corresponding countermeasures is conducted, focusing on aggregation algorithms that provide robustness against Byzantine failures. Following this direction, new aggregation algorithms are observed on a set of simulations that recreate realistic scenarios, in the absence and presence of Byzantine adversaries. These combine the Distance-based Krum approach with the Statistical based aggregation algorithm. Achieved results demonstrate the functionality of the proposed solutions in terms of accuracy and convergence rounds in comparison with well-known federated algorithms under a correct and incorrect estimation of the attackers number.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"9 8","pages":"1034-1035"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natively Secure 6G IoT Using Intelligent Physical Layer Security 利用智能物理层安全技术确保 6G 物联网的本机安全

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454737

Israt Ara, Brian Kelley

引用次数: 0

Anti-Money Laundering in Cryptocurrencies Through Graph Neural Networks: A Comparative Study 通过图神经网络在加密货币中反洗钱：比较研究

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454631

Simone Marasi, Stefano Ferretti

{"title":"Anti-Money Laundering in Cryptocurrencies Through Graph Neural Networks: A Comparative Study","authors":"Simone Marasi, Stefano Ferretti","doi":"10.1109/CCNC51664.2024.10454631","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454631","url":null,"abstract":"Money laundering in cryptocurrencies is a significant concern, as it facilitates and conceals crime and can distort markets and the broader financial system. To combat this issue, researchers have turned to techniques to develop effective Anti-Money Laundering (AML) frameworks. The findings contribute to the ongoing efforts to promote social good by reducing the impact of criminal activities on society. By preventing money laundering, we can also help to combat other criminal activities such as drug trafficking, corruption, and terrorism. This paper focuses on the use of Graph Neural Networks (GNNs) to classify cryptocurrencies transactions. Specifically, the study employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GAT), the Chebyshev spatial convolutional neural network (ChebNet), and GraphSAGE network to classify Bitcoin transactions. The study finds that ChebNet, GraphSAGE and a variant of GAT outperform other methods and improve upon the state of the art in terms of recall and F1 scores, thus suggesting that they can be more reliable in identifying illicit transactions.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"11 8","pages":"272-277"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Downlink RIS-NOMA with Constellation Adjustment for 6G Wireless Communication Systems 针对 6G 无线通信系统的下行 RIS-NOMA 与星座调整

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454823

Ju Yeong Baek, Young-Seok Lee, Bang Chul Jung

引用次数: 0

Challenges of Modeling Participant Behavior in CrowdSensing Evaluation 人群感应评估中模拟参与者行为的挑战

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454892

Christine Bassem

引用次数: 0

Framework: Clustering-Driven Approach for Base Station Parameter Optimization and Automation (CeDA-BatOp) 框架：基站参数优化和自动化的聚类驱动方法（CeDA-BatOp）

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454773

Sudharshan Paindi Jayakumar, Alberto Conte

{"title":"Framework: Clustering-Driven Approach for Base Station Parameter Optimization and Automation (CeDA-BatOp)","authors":"Sudharshan Paindi Jayakumar, Alberto Conte","doi":"10.1109/CCNC51664.2024.10454773","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454773","url":null,"abstract":"The growing demand for fast and reliable wireless services has led to the deployment of more base stations, which has made manual optimization of base station parameters more complex and time-consuming. This can lead to suboptimal network performance and a poor user experience. To address this challenge, we propose a Clustering-Driven Approach for Base Station Parameter Optimization and Automation (CeDA-BatOp), an automated framework for predicting optimized base station parameters. Our framework first compares three clustering algorithms: K-means, DBSCAN, and Agglomerative Clustering, selecting the most suitable one for specific scenarios based on their unique attributes. Simultaneously, our framework leverages machine learning (ML) algorithms to predict the optimal parameters for each base station with an evaluation of multiple ML models to identify the best fit for our data. It also incorporates data drift monitoring to track gradual changes in data distribution over time, ensuring ML model accuracy through periodic retraining. In the simulated scenario, our framework achieved an average of 76% reduction in memory overhead and simplified training by utilizing fewer models, effectively minimizing computational resources. The drift detection system demonstrated an exceptional accuracy of 98.87%, outperforming other cases. These results highlight the potential of our framework to significantly benefit network operators by automating base station parameter tuning, reducing human involvement, and substantially improving network performance and cost savings.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"102 7","pages":"1026-1029"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Cloud-Oriented Indoor-Outdoor Real-Time Localization IoT Architecture for Industrial Environments 面向工业环境的云端室内外实时定位物联网架构

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454636

Laura Belli, Luca Davoli, Gianluigi Ferrari

引用次数: 0

Cloud Storage Workload Characterization: An Approach with Time-Series Analysis 云存储工作量特征描述：时间序列分析法

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454778

Abiola Adegboyega

引用次数: 0

On the Interaction with Large Language Models for Web Accessibility: Implications and Challenges 网络无障碍与大型语言模型的互动：影响与挑战

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454680

Giovanni Delnevo, Manuel Andruccioli, S. Mirri

引用次数: 0

On the Analysis of Model Poisoning Attacks Against Blockchain-Based Federated Learning 基于区块链的联合学习的模型中毒攻击分析

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454875

Rukayat Olapojoye, Mohamed Baza, Tara Salman

引用次数: 0