X-FuseRLSTM:物联网中的跨域可解释的入侵检测框架,使用注意引导的双路特征融合和剩余LSTM.
1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了X-FuseRLSTM,这是一个用于物联网网络的先进入侵检测系统. 它在识别各种环境中的威胁方面实现了高准确性,增强了网络安全.
科学领域:
- 网络安全和网络工程 网络安全和网络工程
- 人工智能和机器学习
- 物联网 (IoT) 安全 安全 物联网
背景情况:
- 在异质和动态的物联网系统中,入侵检测面临挑战,原因是域名的变化和不断变化的攻击策略.
- 现有的方法难以跨域概括,并提供可解释的威胁识别.
研究的目的:
- 提出一种新的算法,X-FuseRLSTM,用于在物联网环境中有效的跨域入侵检测.
- 通过将空间和时间的相关性纳入网络流量数据来增强检测能力.
- 为模型预测提供可解释的人工智能 (XAI).
主要方法:
- 一个由注意力引导的双路特征融合框架,并与剩余的LSTM (长短期记忆) 架构相结合.
- 使用深度编码器和稀疏变压器提取特征,然后进行特征融合和减少.
- 使用深度神经网络和RLSTM进行分类,并使用XAI技术进行模型解释.
主要成果:
- 在多个数据集中,X-FuseRLSTM实现了高精度:在TON_IoT网络上99.40%,在NSL-KDD上99.72%,在CICIoMT2024上97.66% (19级) 和98.05% (6级).
- 该模型展示了强大的域泛化能力.
- 集成XAI为入侵检测预测提供了可解释性.
结论:
- 在各种物联网系统中,X-FuseRLSTM提供了强大的入侵检测解决方案.
- 该算法的有效性,域概括性和可解释性使其适用于实际的物联网安全应用.
- 该方法平衡了高检测精度和计算效率.
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