Metrics
29 Record Views
Journal article
Node Classification of Network Threats Leveraging Graph-Based Characterizations Using Memgraph
Computers (Basel), Vol.13(7), p.171
07/15/2024
Web of Science ID: WOS:001277898300001
Abstract
This research leverages Memgraph, an open-source graph database, to analyze graph-based network data and apply Graph Neural Networks (GNNs) for a detailed classification of cyberattack tactics categorized by the MITRE ATT&CK framework. As part of graph characterization, the page rank, degree centrality, betweenness centrality, and Katz centrality are presented. Node classification is utilized to categorize network entities based on their role in the traffic. Graph-theoretic features such as in-degree, out-degree, PageRank, and Katz centrality were used in node classification to ensure that the model captures the structure of the graph. The study utilizes the UWF-ZeekDataFall22 dataset, a newly created dataset which consists of labeled network logs from the University of West Florida’s Cyber Range. The uniqueness of this study is that it uses the power of combining graph-based characterization or analysis with machine learning to enhance the understanding and visualization of cyber threats, thereby improving the network security measures.
Files and links (1)
Related links
Details
- Title
- Node Classification of Network Threats Leveraging Graph-Based Characterizations Using Memgraph
- Publication Details
- Computers (Basel), Vol.13(7), p.171
- Resource Type
- Journal article
- Publisher
- MDPI AG
- Grant note
- This research was funded by the National Centers of Academic Excellence in Cybersecurity, NCAE-C-002: Cyber Research Innovation Grant Program, Grant Number: H98230-21-1-0170. We would also like to thank the Askew Institute at University of West Florida for partially supporting this grant.
- Copyright
- © 2024 by the authors.
- Identifiers
- WOS:001277898300001; 99380566267806600
- Academic Unit
- Computer Science; Hal Marcus College of Science and Engineering ; Mathematics and Statistics
- Language
- English