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IDMM-IDS:基于反转的迪里克莱特混合模型的物联网高效和强大的入侵检测系统

Wenda He¹, Xiangrui Cai¹, Yiying Yu²

¹College of Computer Science, TKLNDST, Nankai University, Tianjin, China.

Neural networks : the official journal of the International Neural Network Society

|August 28, 2025

概括

这项研究介绍了IDMM-IDS,它是物联网 (IoT) 的高效入侵检测系统. 它有效地检测资源有限的环境中的威胁,并减少计算负载.

科学领域:

网络安全
网络安全
物联网 (物联网)

背景情况:

物联网 (IoT) 设备的扩散造成了重大安全漏洞.
传统的入侵检测系统 (IDS) 往往不适合资源有限的物联网环境,原因是计算需求高,适应能力差.
需要针对物联网安全的高效和强大的IDS.

研究的目的:

提出IDMM-IDS,一个专门为物联网 (IoT) 设计的高效和强大的入侵检测系统.
解决物联网入侵检测中的计算开销和适应性的挑战.
在不平衡的数据集中增强少数群体威胁的检测.

主要方法:

使用倒置的迪里克莱特混合模型 (IDMM) 以最小的计算开销来建模复杂的网络流量.
使用扩展的随机变量推理 (ESVI) 进行高效的模型训练和推理.
整合了一种基于集群的超标采样技术来处理类不平衡问题.

主要成果:

与现有的UNSW-NB15,WSN-DS和WUSTL-IIOT-2021数据集相比,IDMM-IDS的检测性能更高.
拟议的系统显著减少了培训和决策时间,显示了其效率.
在没有将噪音引入数据集的情况下,可以有效地检测少数类威胁.

结论:

IDMM-IDS是一个高效和强大的入侵检测系统,适用于资源有限的物联网环境.
IDMM和ESVI的结合为分析物联网网络流量提供了强大的方法.
综合过量采样技术有效地解决了类不平衡,提高了威胁检测能力.

关键词:

贝叶斯推理阶级不平衡扩展的随机变化推理物联网 (物联网)侵入检测系统

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