TY - JOUR
T1 - 基于图神经网络的动态网络异常检测算法
AU - Guo, Jia Yan
AU - Li, Rong Hua
AU - Zhang, Yan
AU - Wang, Guo Ren
N1 - Publisher Copyright:
© Copyright 2020, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Dynamic graph structured data is ubiquitous in real-life applications. Mining outliers on dynamic networks is an important problem, which is very useful for many practical applications. Most traditional network outlier detection algorithms focus mainly on the strutraulal anomaly, ignoring the nodes and edges' attributes, and the time-varying features as well. This study proposes a graph neural network based network anomaly detection algorithm which can capture the nodes and edges' attributes and time-varying features and fully uses these features to learn a representation vector for each node. Specifically, the proposed algorithm improves an unsupervised graph neural network framework called DGI. Based on DGI, a new danamic DGI algorithm is proposed, which is called Dynamic-DGI, for dynamic networks. Dynamic-DGI can simultaneously extracts the abnormal characteristics of the network itself and the abnormal characteristics of the network changes. The experimental results show that the proposed algorithm is better than the state-of-the-art anomaly detection algorithm SpotLight, and is significantly better than the traditional network representation learning algorithms. In addition to improving the accuracy, the proposed algorithmis also able to mine interesting anomalies in the network.
AB - Dynamic graph structured data is ubiquitous in real-life applications. Mining outliers on dynamic networks is an important problem, which is very useful for many practical applications. Most traditional network outlier detection algorithms focus mainly on the strutraulal anomaly, ignoring the nodes and edges' attributes, and the time-varying features as well. This study proposes a graph neural network based network anomaly detection algorithm which can capture the nodes and edges' attributes and time-varying features and fully uses these features to learn a representation vector for each node. Specifically, the proposed algorithm improves an unsupervised graph neural network framework called DGI. Based on DGI, a new danamic DGI algorithm is proposed, which is called Dynamic-DGI, for dynamic networks. Dynamic-DGI can simultaneously extracts the abnormal characteristics of the network itself and the abnormal characteristics of the network changes. The experimental results show that the proposed algorithm is better than the state-of-the-art anomaly detection algorithm SpotLight, and is significantly better than the traditional network representation learning algorithms. In addition to improving the accuracy, the proposed algorithmis also able to mine interesting anomalies in the network.
KW - Anomaly detection in dynamic network
KW - Deep learning on graphs
KW - Graph neural network
UR - http://www.scopus.com/inward/record.url?scp=85083032593&partnerID=8YFLogxK
U2 - 10.13328/j.cnki.jos.005903
DO - 10.13328/j.cnki.jos.005903
M3 - 文章
AN - SCOPUS:85083032593
SN - 1000-9825
VL - 31
SP - 748
EP - 762
JO - Ruan Jian Xue Bao/Journal of Software
JF - Ruan Jian Xue Bao/Journal of Software
IS - 3
ER -