Relentless Firefly Optimization-Based Routing Protocol (RFORP) for Securing Fintech Data in IoT-Based Ad-Hoc Networks

Q4 Computer Science
J. Ramkumar, K. S. Jeen Marseline, D. R. Medhunhashini
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引用次数: 3

Abstract

– The widespread adoption of Internet of Things (IoT) technology and the rise of fintech applications have raised concerns regarding the secure and efficient routing of data in IoT-based ad-hoc networks (IoT-AN). Challenges in this context include vulnerability to security breaches, potential malicious node presence, routing instability, and energy inefficiency. This article proposes the Relentless Firefly Optimization-based Routing Protocol (RFORP) to overcome these issues. Inspired by fireflies’ natural behaviour, RFORP incorporates relentless firefly optimization techniques to enhance packet delivery, malicious node detection, routing stability, and overall network resilience. Simulation results demonstrate RFORP’s superiority over existing protocols, achieving higher packet delivery ratios, accurate malicious node detection, improved routing stability, and significant energy efficiency. The proposed RFORP offers a promising solution for securing fintech data in IoT-AN, providing enhanced performance, reliability, and security while effectively addressing the identified challenges. This research contributes to advancing secure routing protocols in fintech applications and guides network security and protocol selection in IoT environments.
在基于物联网的Ad Hoc网络中保护金融科技数据的基于无情萤火虫优化的路由协议(RFORP)
-物联网(IoT)技术的广泛采用和金融科技应用的兴起,引发了人们对基于物联网的自组织网络(IoT- an)中安全有效的数据路由的担忧。这方面的挑战包括易受安全破坏、潜在恶意节点存在、路由不稳定和能源效率低下。本文提出了基于无情萤火虫优化的路由协议(RFORP)来克服这些问题。受萤火虫自然行为的启发,RFORP结合了无情的萤火虫优化技术,以增强数据包传输、恶意节点检测、路由稳定性和整体网络弹性。仿真结果表明,RFORP优于现有协议,实现了更高的数据包投递率、准确的恶意节点检测、更好的路由稳定性和显著的能效。拟议的RFORP为IoT-AN中的金融科技数据保护提供了一个有前途的解决方案,提供增强的性能、可靠性和安全性,同时有效地应对已确定的挑战。本研究有助于推进金融科技应用中的安全路由协议,并指导物联网环境中的网络安全和协议选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Computer Networks and Applications
International Journal of Computer Networks and Applications Computer Science-Computer Science Applications
CiteScore
2.30
自引率
0.00%
发文量
40
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