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变压器和RBF的融合用于传感器网络中的异常流量检测
Aibing Dai1, Jianwei Guo1, Yuanyuan Hou1
1Beijing Academy of Science and Technology, Beijing 100089, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了RESTADM,这是一种使用变压器和辐射基函数 (RBF) 神经网络的新型传感器异常检测模型. 通过准确识别物联网数据中的异常,RESTADM显著提高了智能传感系统的安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 设备的扩散产生了大量,复杂的数据,增加了传感器网络的安全漏洞.
- 异常检测对于保持这些传感器网络的稳定性和安全性至关重要.
研究的目的:
- 提出RESTADM,一个新的传感器异常检测模型,集成变压器和辐射基函数 (RBF) 神经网络.
- 通过精确的异常识别来提高智能传感系统的安全性和可靠性.
主要方法:
- 拟议的RESTADM模型使用变压器来捕获传感器数据中的时间依赖.
- 辐射基函数 (RBF) 神经网络用于准确识别传感器数据中的异常.
- 该模型结合了变压器和RBF组件,以实现对异常检测的协同方法.
主要成果:
- 在SMD和PSM基准数据集上,RESTADM实现了最先进的性能.
- 该模型在SMD上获得了98.56%的F1分,在PSM上获得了97.70%.
- 与包括CNN,LSTM和标准变压器在内的基线模型相比,在统计学上有显著的改进.
结论:
- 在RESTADM中提出的变压器-RBF融合验证了其高精度和稳定性.
- RESTADM为面对日益复杂的数据的智能传感系统提供了有效和高效的安全解决方案.
- 该模型的性能突显了混合深度学习方法在物联网环境中强大的异常检测的潜力.
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