基于改进HMM的物联网入侵路径检测方法

何洋军

doi:10.11959/j.issn.2096-109x.AQ26047

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基于改进HMM的物联网入侵路径检测方法

学术论文 | 更新时间：2026-07-15

- 基于改进HMM的物联网入侵路径检测方法
- Internet of things intrusion path detection method based on improved HMM
- 网络与信息安全学报 2026年12卷第3期页码：167-178
- 作者机构：
  
  浙江省公众信息产业有限公司，浙江杭州310012
- 作者简介：
  
  何洋军（1988- ），男，浙江省公众信息产业有限公司高级工程师，主要研究方向为网络安全、数据安全。
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.AQ26047
  中图分类号： TP115
- 收稿：2026-02-27，
  
  修回：2026-05-16，
  
  纸质出版：2026-06-25
- 稿件说明：
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何洋军. 基于改进HMM的物联网入侵路径检测方法[J]. 网络与信息安全学报, 2026, 12(3): 167-178.

He Yangjun. Internet of things intrusion path detection method based on improved HMM[J]. Chinese Journal of Network and Information Security, 2026, 12(3): 167-178.
何洋军. 基于改进HMM的物联网入侵路径检测方法[J]. 网络与信息安全学报, 2026, 12(3): 167-178. DOI： 10.11959/j.issn.2096-109x.AQ26047.

He Yangjun. Internet of things intrusion path detection method based on improved HMM[J]. Chinese Journal of Network and Information Security, 2026, 12(3): 167-178. DOI： 10.11959/j.issn.2096-109x.AQ26047.

摘要

针对物联网多步入侵路径隐蔽性强、标注数据稀缺及加密流量占比高等难题，对基于改进隐马尔可夫模型（HMM）的入侵路径检测进行了研究。构建融合网络拓扑、配置与漏洞信息的四元组入侵图模型，利用逻辑斯蒂函数量化状态转移概率，形成动态概率矩阵。针对传统HMM的零概率缺陷，设计基于语义相似性的最大似然概率重分配策略。引入状态转移修正系数与自适应动态阈值，构建多步迭代推断框架，实现告警序列智能筛选与入侵意图鲁棒识别。实验结果表明，该方法在复杂攻击场景下路径检测准确率达94%以上，具备良好的鲁棒性与可扩展性。

Abstract

A study was conducted on intrusion path detection based on an improved hidden Markov model (HMM) to address the challenges of strong concealment of multi-step intrusion paths in the Internet of Things

scarce annotated data

and high proportion of encrypted traffic. A quadruple intrusion graph model was constructed that integrated network topology

configuration

and vulnerability information

state transition probability was quantified using logistic functions

a dynamic probability matrix was formed

and a maximum likelihood probability reallocation strategy was designed based on semantic similarity for zero probability defects in traditional HMM. Introducing state transition correction coefficients and adaptive dynamic thresholds

a multi-step iterative inference framework was constructed to achieve intelligent screening of alarm sequences and robust recognition of intrusion intentions. The experimental results show that the method has an accuracy of over 94% in path detection in complex attack scenarios

and has good robustness and scalability.

关键词

Keywords

references

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