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GraphFedAI框架用于在物联网系统中检测DDoS攻击,使用联合学习和基于图形的人工智能.

Mohd Anjum1, Ashit Kumar Dutta2, Ali Elrashidi3

  • 1Department of Computer Engineering, Aligarh Muslim University, 202002, Aligarh, India.

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|August 1, 2025
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概括
此摘要是机器生成的。

本研究介绍了GraphFedAI,这是一个用于检测物联网 (IoT) 中分布式拒绝服务 (DDoS) 攻击的新框架. 通过自适应式图形建模和联合学习,GraphFedAI增强了安全性,确保了隐私和可扩展性.

关键词:
错误的阳性率是一个错误的阳性率.联合学习是联合学习.基于图形的神经网络.物联网的物联网,就是物联网.隐私 隐私 隐私 隐私 隐私 隐私可扩展性 可扩展性

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 网络工程 网络工程

背景情况:

  • 物联网 (IoT) 带来了重大的安全和隐私挑战,特别是分布式拒绝服务 (DDoS) 攻击.
  • 传统的DDoS检测方法在动态物联网环境中与隐私,可扩展性和适应性作斗争.

研究的目的:

  • 提出GraphFedAI,一个用于在异质物联网网络中进行强大,可扩展和保护隐私的DDoS检测的新框架.
  • 通过整合先进的技术来提高安全性来解决传统方法的局限性.

主要方法:

  • 使用基于会话的自适应图形建模来将物联网网络表示为动态图形.
  • 采用皮尔森相关性指导的特征选择和插值感知图形神经网络 (GNN) 培训.
  • 整合联合学习 (FL) 以保护隐私,本地化模型培训和增强可扩展性.

主要成果:

  • 在动态物联网条件下,GraphFedAI展示了对DDoS攻击的高检测准确性.
  • 该框架实现了低虚假阳性率,表明可靠的表现.
  • 评估证实了CIC-IoT-2023数据集的强有力的弹性和有效性.

结论:

  • GraphFedAI在保护物联网网络免受DDoS攻击方面取得了重大进展.
  • 该框架整合了GNN和联合学习,提供了一个可扩展和保护隐私的解决方案.
  • 这种方法提高了异质物联网生态系统的整体安全态度.