改进自适应模型池的在线异常检测算法

项秋艳; 訾玲玲; 丛鑫

doi:10.12263/DZXB.20230731

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改进自适应模型池的在线异常检测算法

学术论文 | 更新时间：2025-12-24

- 改进自适应模型池的在线异常检测算法
- Improved Adaptive Model Pools for Online Anomaly Detection Algorithms
- 电子学报 2024年52卷第7期页码：2503-2514
- 作者机构：
  
  重庆师范大学计算机与信息科学学院，重庆 401331
- 作者简介：
  
  [ "项秋艳女，1998年7月出生，湖南省长沙人.重庆师范大学计算机与信息科学学院计算机技术专业硕士.研究方向为深度学习、异常检测、数据可视化.E-mail: 2021210516082@stu.cqnu.edu.cn" ]
  [ "訾玲玲女，1981年12月出生，辽宁阜新人.重庆师范大学计算机与信息科学学院副教授.研究方向为深度学习、计算机视觉、多媒体处理.E-mail: lingling19812004@126.com" ]
  [ "丛鑫男，1982年2月出生，辽宁阜新人.重庆师范大学计算机与信息科学学院副教授.研究方向为云计算、虚拟网络映射、深度学习." ]
- 基金信息：
  
  重庆市教育科学规划重点课题(K22YE205098)
- DOI：10.12263/DZXB.20230731
  中图分类号： TP391;
- 收稿：2023-08-03，
  
  修回：2024-05-14，
  
  纸质出版：2024-07-25
- 稿件说明：
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项秋艳, 訾玲玲, 丛鑫. 改进自适应模型池的在线异常检测算法[J]. 电子学报, 2024, 52(07): 2503-2514.

XIANG Qiu-yan, ZI Ling-ling, CONG Xin. Improved Adaptive Model Pools for Online Anomaly Detection Algorithms[J]. Acta Electronica Sinica, 2024, 52(07): 2503-2514.
项秋艳, 訾玲玲, 丛鑫. 改进自适应模型池的在线异常检测算法[J]. 电子学报, 2024, 52(07): 2503-2514. DOI：10.12263/DZXB.20230731

XIANG Qiu-yan, ZI Ling-ling, CONG Xin. Improved Adaptive Model Pools for Online Anomaly Detection Algorithms[J]. Acta Electronica Sinica, 2024, 52(07): 2503-2514. DOI：10.12263/DZXB.20230731

摘要

精确的在线异常检测方法是物联网行业发展的核心，其中，以复杂和动态数据流为对象的在线异常识别是研究热点.现有在线异常检测方法存在处理复杂性负载过重问题，离线深度异常检测方法则存在因数据分布变化导致概念漂移问题.针对上述问题，本文提出了改进自适应模型池的在线异常检测框架，该框架可以与基于自动编码器的异常检测方法协作实现在线异常检测.首先，利用基于自动编码器的异常检测模型进行基本异常识别；其次，以自适应模型池为基础，融合概念漂移检测算法准确识别概念漂移，适应动态变化的数据流，解决概念漂移现象；最后，优化自适应模型池的模型合并方法，提升在线异常识别能力.实验结果表明，相比自动编码器模型的流变体和原自适应模型池算法，提出的算法在异常检测精度指标上分别提升了20.2%和5.83%，同时，最佳精度指标高于现有在线异常检测算法约16.7%.

Abstract

Accurate online anomaly detection methods are at the core of the development of IoT-related industries

in which online anomaly identification targeting complex and dynamic data streams is one of the important research hotspots. Existing online anomaly detection methods suffer from the problem of processing complexity overload

while offline deep anomaly detection methods suffer from the problem of concept drift due to the change of data distribution. To address the above problems

this paper proposes an online anomaly detection framework with improved adaptive model pooling

which can collaborate with autoencoder-based anomaly detection methods to achieve online anomaly detection. Firstly

the basic anomaly identification is carried out using the autoencoder-based anomaly detection model. Secondly

based on the adaptive model pool

the concept drift detection algorithm is integrated to accurately identify concept drift

adapt to the dynamically changing data flow

and solve the concept drift phenomenon. Finally

the model merging method of the optimised adaptive model pool is optimised

which enhances the capability of online anomaly identification. The experimental results show that compared with the flow variant of autoencoder model and the original adaptive model pool algorithm

the proposed algorithm improves the anomaly detection accuracy indexes by 20.2% and 5.83% respectively

and meanwhile is higher than the existing online anomaly detection algorithms in the best accuracy indexes by about 16.7%.

关键词

Keywords

references

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