基于OS-RVFL神经网络安全雾计算的IDS密钥协议认证方案设计

IF 8.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shreeya Swagatika Sahoo;Dibyasundar Das
{"title":"基于OS-RVFL神经网络安全雾计算的IDS密钥协议认证方案设计","authors":"Shreeya Swagatika Sahoo;Dibyasundar Das","doi":"10.1109/JIOT.2025.3566962","DOIUrl":null,"url":null,"abstract":"Cloud computing technology was first introduced by Amazon in 2006 and has provided customers with high-quality services through the Internet. However, the growing number of IoT and mobile devices has led to challenges such as latency, power consumption, and network strain. To address these issues, Cisco developed fog computing, which enhances cloud capabilities by processing data closer to the network edge. Despite its advantages, security concerns remain a critical challenge within the fog computing paradigm. The issue with secure authentication in fog often involves balancing the need for robust security measures with the constraints of limited resources and varying trust levels among distributed devices. Moreover, the security protocols are weak against unknown threats, leaving the system vulnerable to potential attacks. To address these issues, the study proposes an online sequential random vector functional link (OS-RVFL) neural network-based authentication approach. This method adapts to security threats in real time, improves authentication resilience, and efficiently uses available resources. This intrusion detection model is integrated into the proposed lightweight authentication protocol to enhance the overall security framework in the fog environment. This ensures a dynamic response to emerging threats while maintaining a low resource footprint across the fog computing environment. The proposed system can also learn and update in real-time without depending on the previous training data batch. The effectiveness of the proposed detection model was evaluated using the NSL-KDD dataset, achieving an accuracy of 0.8943, precision of 0.9142, recall of 0.8986, and an F1-score of 0.9064. The security of the proposed scheme has been rigorously analyzed using the real-or-random model, which provides formal proof of its robustness. Furthermore, the scheme has been verified using the widely accepted AVISPA tool. In addition, compared to other related works, the proposed scheme stands out with its lower communication and storage costs, making it more efficient and reliable.","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"12 14","pages":"28521-28530"},"PeriodicalIF":8.9000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10985869","citationCount":"0","resultStr":"{\"title\":\"Design of Key Agreement Authentication Scheme With IDS Using OS-RVFL Neural Network for Secure Fog Computing\",\"authors\":\"Shreeya Swagatika Sahoo;Dibyasundar Das\",\"doi\":\"10.1109/JIOT.2025.3566962\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cloud computing technology was first introduced by Amazon in 2006 and has provided customers with high-quality services through the Internet. However, the growing number of IoT and mobile devices has led to challenges such as latency, power consumption, and network strain. To address these issues, Cisco developed fog computing, which enhances cloud capabilities by processing data closer to the network edge. Despite its advantages, security concerns remain a critical challenge within the fog computing paradigm. The issue with secure authentication in fog often involves balancing the need for robust security measures with the constraints of limited resources and varying trust levels among distributed devices. Moreover, the security protocols are weak against unknown threats, leaving the system vulnerable to potential attacks. To address these issues, the study proposes an online sequential random vector functional link (OS-RVFL) neural network-based authentication approach. This method adapts to security threats in real time, improves authentication resilience, and efficiently uses available resources. This intrusion detection model is integrated into the proposed lightweight authentication protocol to enhance the overall security framework in the fog environment. This ensures a dynamic response to emerging threats while maintaining a low resource footprint across the fog computing environment. The proposed system can also learn and update in real-time without depending on the previous training data batch. The effectiveness of the proposed detection model was evaluated using the NSL-KDD dataset, achieving an accuracy of 0.8943, precision of 0.9142, recall of 0.8986, and an F1-score of 0.9064. The security of the proposed scheme has been rigorously analyzed using the real-or-random model, which provides formal proof of its robustness. Furthermore, the scheme has been verified using the widely accepted AVISPA tool. In addition, compared to other related works, the proposed scheme stands out with its lower communication and storage costs, making it more efficient and reliable.\",\"PeriodicalId\":54347,\"journal\":{\"name\":\"IEEE Internet of Things Journal\",\"volume\":\"12 14\",\"pages\":\"28521-28530\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-03-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10985869\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Internet of Things Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10985869/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Internet of Things Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10985869/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

云计算技术最早由亚马逊于2006年推出,通过互联网为客户提供高质量的服务。然而,越来越多的物联网和移动设备带来了诸如延迟、功耗和网络压力等挑战。为了解决这些问题,思科开发了雾计算,通过处理更靠近网络边缘的数据来增强云功能。尽管雾计算具有诸多优势,但安全问题仍然是雾计算范式中的一个关键挑战。雾中的安全身份验证问题通常涉及到如何平衡健壮的安全措施需求与有限资源的约束以及分布式设备之间不同的信任级别。此外,安全协议对未知威胁的防御能力较弱,使系统容易受到潜在的攻击。为了解决这些问题,本研究提出了一种基于在线顺序随机向量功能链接(OS-RVFL)神经网络的身份验证方法。该方法实时适应安全威胁,提高认证弹性,有效利用可用资源。将该入侵检测模型集成到所提出的轻量级身份验证协议中,增强了雾环境下的整体安全框架。这确保了对新出现的威胁的动态响应,同时在整个雾计算环境中保持低资源占用。该系统还可以实时学习和更新,而不依赖于以前的训练数据批次。利用NSL-KDD数据集对所提出的检测模型进行了有效性评估,准确率为0.8943,精密度为0.9142,召回率为0.8986,f1得分为0.9064。采用实或随机模型对该方案的安全性进行了严格的分析,给出了其鲁棒性的形式化证明。此外,该方案已使用广泛接受的AVISPA工具进行了验证。此外,与其他相关工作相比,该方案具有较低的通信和存储成本,使其更加高效可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of Key Agreement Authentication Scheme With IDS Using OS-RVFL Neural Network for Secure Fog Computing
Cloud computing technology was first introduced by Amazon in 2006 and has provided customers with high-quality services through the Internet. However, the growing number of IoT and mobile devices has led to challenges such as latency, power consumption, and network strain. To address these issues, Cisco developed fog computing, which enhances cloud capabilities by processing data closer to the network edge. Despite its advantages, security concerns remain a critical challenge within the fog computing paradigm. The issue with secure authentication in fog often involves balancing the need for robust security measures with the constraints of limited resources and varying trust levels among distributed devices. Moreover, the security protocols are weak against unknown threats, leaving the system vulnerable to potential attacks. To address these issues, the study proposes an online sequential random vector functional link (OS-RVFL) neural network-based authentication approach. This method adapts to security threats in real time, improves authentication resilience, and efficiently uses available resources. This intrusion detection model is integrated into the proposed lightweight authentication protocol to enhance the overall security framework in the fog environment. This ensures a dynamic response to emerging threats while maintaining a low resource footprint across the fog computing environment. The proposed system can also learn and update in real-time without depending on the previous training data batch. The effectiveness of the proposed detection model was evaluated using the NSL-KDD dataset, achieving an accuracy of 0.8943, precision of 0.9142, recall of 0.8986, and an F1-score of 0.9064. The security of the proposed scheme has been rigorously analyzed using the real-or-random model, which provides formal proof of its robustness. Furthermore, the scheme has been verified using the widely accepted AVISPA tool. In addition, compared to other related works, the proposed scheme stands out with its lower communication and storage costs, making it more efficient and reliable.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Internet of Things Journal
IEEE Internet of Things Journal Computer Science-Information Systems
CiteScore
17.60
自引率
13.20%
发文量
1982
期刊介绍: The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信