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Updated: May 16, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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一个用于物联网安全的计算框架,集成基于深度学习的语义算法,用于实时威胁响应.

Ripal Ranpara1, Shobhit K Patel2, Om Prakash Kumar3

  • 1Faculty of Computer Applications, Marwadi University, Rajkot, 360003, India.

Scientific reports
|May 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种混合深度学习和语义推理框架,用于增强物联网 (IoT) 安全性. 它改善了物联网网络中的实时威胁检测和自主响应.

关键词:
基于人工智能的异常检测检测.情境意识的安全性 情境意识的安全性网络安全 网络安全深度学习是一种深度学习.这就是为什么物联网物联网物联网.知识图是知识图.实时威胁检测和威胁检测安全框架 安全框架语义计算是一种语义计算.

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科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 物联网的物联网,就是物联网.

背景情况:

  • 物联网 (IoT) 网络的普及带来了重大安全挑战,特别是在实时威胁检测和响应方面.
  • 现有的安全解决方案经常与针对物联网生态系统的现代网络威胁的动态和复杂性作斗争.

研究的目的:

  • 开发和验证一种新的混合深度学习和语义推理框架,用于先进的物联网威胁情报和自主安全.
  • 加强在物联网环境中对复杂的网络攻击进行低延迟识别和响应的能力.

主要方法:

  • 卷积神经网络 (CNN) 的集成用于空间异常检测和循环神经网络 (RNN) 的连续模式识别.
  • 实施语义上下文化层,使用知识图来实现上下文感知威胁检测.
  • 边缘计算和实时流处理模式的应用,以实现高效,低延迟的威胁分析.

主要成果:

  • 在识别高级持久威胁 (APT) 和分布式拒绝服务 (DDoS) 攻击方面表现出高准确性,可扩展性和适应性.
  • 使用CICIoT 2023数据集和定制物联网测试台验证了框架的计算和能源效率.
  • 实现了对实时响应至关重要的低延迟威胁识别.

结论:

  • 拟议的混合框架有效地弥合了深度学习,语义推理和实际物联网安全挑战.
  • 该研究有助于下一代自主物联网安全解决方案,强调负责任的部署与隐私和道德考虑.
  • 未来的工作将集中在现实世界的部署和适应性威胁情报机制上.