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在物联网边缘环境中使用ECaps-Net进行安全数据传输的基于区块链的加密框架.

Islabudeen Mohamed Meerasha1, Jafar Ali Ibrahim Syed Masood2, Thanapal P3

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. islabudeen.m@vit.ac.in.

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概括
此摘要是机器生成的。

本研究介绍了用于物联网 (IoT) 边缘计算的增强入侵检测系统 (IDS). 该系统使用深度学习和区块链来保护数据传输,在检测威胁方面实现高准确性.

关键词:
区块链 区块链 区块链 区块链边缘计算是一种边缘计算.增强的囊网络物联网的物联网,就是物联网.侵入检测入侵检测系统默克尔-达姆加德的加密算法安全的数据传输.挤压和激发可以进行.

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科学领域:

  • 网络安全 网络安全
  • 计算机网络 计算机网络
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 和边缘计算由于分布式数据处理而带来了独特的安全挑战.
  • 传统的基于云的安全性对于动态的物联网环境是不够的,需要边缘级别的解决方案.
  • 侵入检测系统 (IDS) 对于实时监控和缓解物联网生态系统中的攻击至关重要.

研究的目的:

  • 建议基于增强深度学习 (DL) 的IDS与基于区块链的加密算法集成,用于安全的物联网边缘计算.
  • 在分布式物联网环境中增强数据隐私,完整性和安全性.
  • 开发针对云服务器和边缘设备的恶意访问的强有力的防御.

主要方法:

  • 数据预处理和入侵数据集的规范化.
  • 使用增强囊网络 (ECaps-Net) 进行分类,其中包含挤压和刺激 (SE) 块.
  • 使用区块链技术和Merkle-Damgard加密哈希,安全地传输数据.

主要成果:

  • 拟议的IDS实现了高准确度,KDD Cup-99的最高准确率为98.90%,UNSW-NB 15数据集的最高准确率为98.78%.
  • 与现有的入侵检测方法相比,其表现优越.
  • 成功保护云服务器和边缘设备免受未经授权的访问.

结论:

  • 集成的DL和区块链框架为物联网边缘环境中的安全数据传输提供了可靠和高效的解决方案.
  • 该ECaps-Net有效地识别和分类网络入侵.
  • 区块链集成确保了数据完整性和机密性,提高了整体物联网安全性.