基于图的异常检测研究进展
作者:
作者单位:

宁波大学

作者简介:

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中图分类号:

TP391

基金项目:

国家自然科学基金(No.61602133);浙江省自然科学基金(No.LY20F020009, No.LZ20F020001)


Research progress of Graph-based Anomaly Detection
Author:
Affiliation:

Ningbo University

Fund Project:

The National Natural Science Foundation of China(No.61602133);The Natural Science Foundation of Zhejiang Province of China(No.LY20F020009, No.LZ20F020001)

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    摘要:

    异常检测是指识别数据集中显著区别于其它正常模式的数据,广泛应用于欺诈检测、入侵检测、数据分析等领域.现有的异常检测研究多是基于非结构化数据点集,而现实世界中数据间复杂的结构关系构成了复杂网络,在数学形式上表示为图,所以面向复杂网络的异常检测的需求日益增加.本文总结了当前复杂网络异常检测的方法与研究进展,首先提出复杂网络异常检测的必要性与发展历史;接着分别从静态图和动态图的视角将复杂网络异常检测分为基于结构、社区、关系学习的静态图异常检测和基于节点、边、子图、全图的动态图异常检测,然后分类别地进行概述、分析与比较,给出了复杂网络异常检测的应用场景;最后总结未来面向复杂网络异常检测的研究方向.

    Abstract:

    Anomaly detection is to identify data that is significantly different from other normal patterns in the data set, and is widely applied in fraud detection, intrusion detection, and data analysis and other fields. Existing researches on anomaly detection are mostly based on unstructured data point sets, and there are complex structural relationships between data to form a complex network in the real world, and the network is represented as a graph in mathematical form, so the demand of anomaly detection for complex networks is increasing. This paper summarizes the current methods and research advances of anomaly detection for complex networks. First, the necessity and development history of anomaly detection for complex networks are proposed. Then, from the perspective of static and dynamic graphs, the anomaly detection for complex networks is divided into static graph anomaly detection based on structure, community,relationship learning, and dynamic graph anomaly detection based on nodes, edges, subgraphs, and full graphs, and then summarize, analyze and compare by category, and the application scenarios of anomaly detection for complex networks are given. Finally, the future research directions of anomaly detection for complex networks are summarized.

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  • 收稿日期:2020-01-12
  • 最后修改日期:2020-12-26
  • 录用日期:2020-09-08
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