Efficient Large-Scale IoT Botnet Detection through GraphSAINT-Based Subgraph Sampling and Graph Isomorphism Network

Lihua Yin, Weizhe Chen, Xi Luo () and Hongyu Yang
Additional contact information
Lihua Yin: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Weizhe Chen: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Xi Luo: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Hongyu Yang: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China

Mathematics, 2024, vol. 12, issue 9, 1-20

Abstract: In recent years, with the rapid development of the Internet of Things, large-scale botnet attacks have occurred frequently and have become an important challenge to network security. As artificial intelligence technology continues to evolve, intelligent detection solutions for botnets are constantly emerging. Although graph neural networks are widely used for botnet detection, directly handling large-scale botnet data becomes inefficient and challenging as the number of infected hosts increases and the network scale expands. Especially in the process of node level learning and inference, a large number of nodes and edges need to be processed, leading to a significant increase in computational complexity and posing new challenges to network security. This paper presents a novel approach that can accurately identify diverse intricate botnet architectures in extensive IoT networks based on the aforementioned circumstance. By utilizing GraphSAINT to process large-scale IoT botnet graph data, efficient and unbiased subgraph sampling has been achieved. In addition, a solution with enhanced information representation capability has been developed based on the Graph Isomorphism Network (GIN) for botnet detection. Compared with the five currently popular graph neural network (GNN) models, our approach has been tested on C2, P2P, and Chord datasets, and higher accuracy has been achieved.

Keywords: botnet detection; GraphSAINT; subgraph sampling; Graph Isomorphism Network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/9/1315/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/9/1315/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:9:p:1315-:d:1383082

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().