通过绿色和可持续的联合学习平台提高物联网安全性:利用高效加密和Quondam签名算法
Turki Aljrees1, Ankit Kumar2, Kamred Udham Singh3
1Department College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al-Batin 39524, Saudi Arabia.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究引入了一种新的方法,结合了高效的数据加密,Quondam签名算法 (QSA) 和联合学习,以增强物联网 (IoT) 对随机攻击的安全性. 综合方法显著降低了通信成本,并提高了针对强大的物联网防御的分析能力.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 物联网 (IoT) 系统面临越来越多的随机攻击,需要采取先进的安全措施.
- 现有的安全协议经常因隐私问题和通信开销而扎.
- 联合学习提供了去中心化的方法来增强物联网中的安全性和隐私.
研究的目的:
- 引入一种新型范式,集成高效的数据加密,Quondam签名算法 (QSA) 和物联网安全的联合学习.
- 为了减轻与中间人攻击和随机威胁相关的漏洞.
- 优化通信成本,增强物联网系统中的分析能力.
主要方法:
- 实施高效的数据加密技术.
- 使用Quondam签名算法 (QSA) 进行安全通信和减轻攻击.
- 联邦式学习的应用,用于去中心化的模型培训和数据聚合,同时保持隐私.
- 通信成本方案的比较分析,包括加密和联合学习方面.
- 使用圆曲线数字签名算法基于时间复杂性的优化在线/离线方案.
主要成果:
- 拟议的方法通过优化位要求,在物联网通信中显著节省成本.
- 联合学习可以安全地汇总和分析来自不同设备的数据,提高分析能力.
- 昆达姆签名算法 (QSA) 有效地减轻了中间人攻击的漏洞.
- 与慢块移动 (SBM) 计划等传统方法相比,综合方案显示了提高效率和降低通信成本.
- 这项研究强调了对一系列物联网攻击的强化弹性.
结论:
- 联合学习和高效加密的协同集成为物联网系统提供了强大的防御机制.
- 拟议的范式提供了显著降低通信成本和提高分析能力.
- 该方法提高了物联网系统对不断变化的威胁的整体安全性和弹性.
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