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可解释的基于深度学习的物联网网络入侵检测系统的性能分析:系统性审查
Taiwo Blessing Ogunseyi1, Gogulakrishan Thiyagarajan2, Honggang He3
1School of Electronic and Information Engineering, Yibin University, Yibin 644000, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
在物联网入侵检测系统 (IDS) 中可解释的AI面临挑战. 高精度通常会损害效率和可解释性,阻碍在边缘设备上部署. 一个新的框架旨在平衡这些因素,实现可靠的物联网安全.
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:
- 物联网入侵检测系统 (IDS) 中的深度学习 (DL) 模型缺乏透明度,影响信任和可靠性.
- 可解释性AI (XAI) 旨在提高可解释性,但其对资源受限物联网性能的影响尚不清楚.
研究的目的:
- 系统地审查可解释的基于DL的IDS对物联网网络的性能权衡.
- 分析检测准确性,计算开销和解释质量.
- 找出缺口,并提出解决方案,以便在实践中部署.
主要方法:
- 按照PRISMA方法的系统文献审查.
- 对129项同行评审研究 (2018-2025) 的分析.
- 研究XAI技术的权衡,DL架构和部署挑战.
主要成果:
- 现有的方法通常以计算效率和可解释性为代价实现高检测精度.
- 这种不平衡限制了在物联网边缘设备上实际部署可解释的IDS.
- 在部署后的XAI评估实践中存在重大差距.
结论:
- 需要一个统一的框架来建模物联网IDS中的性能,效率和可解释性之间的三难题.
- 拟议的XAI评估框架将标准化部署后的指标.
- 提供了可操作的见解,用于开发可靠和高效的物联网可解释的IDS.
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