智能运维（AIOps）中时间异构数据深度异常检测方法综述

桂嘉弈; 马中楠; 周浩; 苏岩; 张苗如; 禹可; 吴晓非

doi:10.1631/FITEE.2400467

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智能运维（AIOps）中时间异构数据深度异常检测方法综述

常规文章 | Updated：2025-10-13

- 智能运维（AIOps）中时间异构数据深度异常检测方法综述
  Enhanced Publication
- Deep anomaly detection of temporal heterogeneous data in AIOps: a survey
- 信息与电子工程前沿（英文） 2025年26卷第9期页码：1551-1576
- Affiliations：
  
  School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
- Author bio：
  
  E-mail: hypatia@bupt.edu.cn
  zhongnanma@bupt.edu.cn
  ‡ Corresponding author
- Funds：
  
  National Natural Science Foundation of China(62371057);111 Project of China(B08004)
- DOI：10.1631/FITEE.2400467
  中图分类号： TP3
- 收稿：2024-06-01，
  
  修回：2024-11-18，
  
  网络出版：2025-09-10，
  
  纸质出版：2025-09
- Accepted：
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桂嘉弈, 马中楠, 周浩, 等. 智能运维（AIOps）中时间异构数据深度异常检测方法综述[J]. 信息与电子工程前沿（英文）, 2025,26(9):1551-1576.

Jiayi GUI, Zhongnan MA, Hao ZHOU, et al. Deep anomaly detection of temporal heterogeneous data in AIOps: a survey[J]. Frontiers of information technology & electronic engineering, 2025, 26(9): 1551-1576.
桂嘉弈, 马中楠, 周浩, 等. 智能运维（AIOps）中时间异构数据深度异常检测方法综述[J]. 信息与电子工程前沿（英文）, 2025,26(9):1551-1576. DOI： 10.1631/FITEE.2400467.

Jiayi GUI, Zhongnan MA, Hao ZHOU, et al. Deep anomaly detection of temporal heterogeneous data in AIOps: a survey[J]. Frontiers of information technology & electronic engineering, 2025, 26(9): 1551-1576. DOI： 10.1631/FITEE.2400467.

摘要

第五代（5G）移动通信及物联网（IoT）技术的进步推动智能应用的发展，但也使得这些网络日益复杂化，并容易遭受各类定向攻击。为监测和识别网络异常事件，研究人员提出多种异常检测（AD）模型，尤其是基于深度学习技术的模型。然而，由于网络运营商缺乏对这些黑箱系统的专业知识，这些模型的部署和使用存在诸多挑战。本文对通信网络领域现有AD模型和方法进行了系统性综述。基于模型原理和结构，将这些模型分为4个方法论类别，并重点强调近期在AD领域中展现巨大潜力的大语言模型的作用。此外，在以下4个应用领域对相关模型展开深入探讨：网络流量监控、网络系统日志分析、云边服务提供以及物联网安全。基于以上应用需求，剖析了当前面临的挑战，并就未来研究方向提出见解，涵盖鲁棒性、可解释性以及大语言模型在AD中的集成作用。

Abstract

The advancement of the fifth generation (5G) mobile communication and Internet of Things (IoT) has facilitated the development of intelligent applications

but has also rendered these networks increasingly complex and vulnerable to various targeted attacks. Numerous anomaly detection (AD) models

particularly those using deep learning technologies

have been proposed to monitor and identify network anomalous events. However

the implementation of these models poses challenges for network operators due to lacking expert knowledge of these black-box systems. In this study

we present a comprehensive review of current AD models and methods in the field of communication networks. We categorize these models into four methodological groups based on their underlying principles and structures

with particular emphasis on the role of recent promising large language models (LLMs) in the field of AD. Additionally

we provide a detailed discussion of the models in the following four application areas: network traffic monitoring

networking system log analysis

cloud and edge service provisioning

and IoT security. Based on these application requirements

we examine the current challenges and offer insights into future research directions

including robustness

explainability

and the integration of LLMs for AD.

关键词

Keywords

references

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