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.

Authors

Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-05-05
DOI
https://doi.org/10.1109/jiot.2025.3566962
Citations
1
Primary Topic
Innovation in Digital Healthcare Systems
Type
article
Field-Weighted Citation Impact
2.94
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design of Key Agreement Authentication Scheme With IDS Using OS-RVFL Neural Network for Secure Fog Computing

Dibyasundar Das, Shreeya Swagatika Sahoo
1 citations
IEEE Internet of Things Journal
Innovation in Digital Healthcare Systems
2.94
article

Design of Key Agreement Authentication Scheme With IDS Using OS-RVFL Neural Network for Secure Fog Computing

Dibyasundar Das, Shreeya Swagatika Sahoo
article en
1 citations

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.

IEEE Internet of Things JournalVol. 12(14)
Siksha O Anusandhan University (IN)
Openalex Percentile: Top 9%
Innovation in Digital Healthcare Systems
2.94
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.