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深度复杂的门隔反复网络基于物联网网络入侵检测系统.

Engy El-Shafeiy1, Walaa M Elsayed2, Haitham Elwahsh3

  • 1Department of Computer Science, Faculty of Computers & Artificial Intelligence, University of Sadat City, Sadat City 32897, Egypt.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度学习入侵检测系统 (IDS),DCGR_IoT,有效地保护物联网 (IoT) 网络. 它通过分析网络流量模式,在检测网络攻击方面达到99.2%的准确性.

关键词:
检测异常检测异常检测复杂的封闭循环网络 (CGRNs) 复杂的封闭循环网络.卷积神经网络 (CNN) 是一种神经网络.深度神经学习是一种深度神经学习.物联网 (IoT) 的物联网 (IoT) 的物联网.侵入检测系统 (IDS) 是一种入侵检测系统.

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科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 的快速扩张需要先进的安全解决方案.
  • 传统的入侵检测系统 (IDS) 难以应对物联网环境的独特挑战,包括设备多样性和实时检测需求.

研究的目的:

  • 提出DCGR_IoT,一种基于深度神经学习的新型IDS,旨在用于双向物联网通信网络.
  • 为了增强物联网环境中的异常检测能力.

主要方法:

  • 使用卷积神经网络 (CNN) 进行空间特征提取和数据过.
  • 采用复杂门式循环网络 (CGRNs) 来进行时间特征提取和多维特征子集构建.
  • 利用CGRNs创建网络流量的详细空间表示,以提取关键特征.

主要成果:

  • 在基准数据集 (UNSW-NB15,KDDCup99,IoT-23) 中,DCGR_IoT表现出高效率.
  • 实现了99.2%的超级检测准确率,可以抵御复杂的网络攻击.
  • 验证了系统在物联网网络中高效准确地进行入侵检测的能力.

结论:

  • DCGR_IoT为保护物联网网络提供了强大而有效的解决方案.
  • 拟议的系统解决了在动态物联网环境中传统IDS的局限性.
  • 突出了深度学习模型在先进物联网安全方面的潜力.