{"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}
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.
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.