Network Anomaly Detection Using a Graph Neural Network

被引：15

作者：

Kisanga, Patrice ^{[1
]}

Woungang, Isaac ^{[1
]}

Traore, Issa ^{[2
]}

Carvalho, Glaucio H. S. ^{[3
]}

机构：

[1] Ryerson Univ, Dept Comp Sci, Toronto, ON, Canada

[2] Univ Victoria, Dept Elect & Comp Engn, Victoria, BC, Canada

[3] Brock Univ, Dept Comp Sci & Engn, St Catharines, ON, Canada

来源：

2023 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS, ICNC | 2023年

关键词：

Anomaly detection; intrusion prevention system; intrusion detection systems; Activity and Event Network (AEN); Graph neural network (GNN); datasets; Graph convolutional network (GCN);

D O I：

10.1109/ICNC57223.2023.10074111

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Contrary to the many traditional network security approaches that focus on volume-based threats, the Activity and Event Network (AEN) is a new approach built on a graph model, which addresses both volumetric attacks and long-term threats that traditional security tools cannot deal with. The AEN graph structural foundation can serve as a basis to construct a graph to be used in Graph Neural Network (GNN) for anomaly and threat detection purposes. In this paper, an AEN-based supervised Graph Convolutional Network (GCN) model is proposed, then evaluated using two labelled datasets, namely, the distributed denial of service (DDoS) and the TOR-nonTOR datasets, yielding an accuracy score of 76% with the DDoS dataset and 88% with the TOR-nonTOR dataset, respectively.

引用

页码：61 / 65

页数：5

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