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Multi-attributed heterogeneous graph convolutional network for bot detection
- Source :
- Information Sciences. 537:380-393
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Bot detection is a fundamental and crucial task for tracing and mitigating cyber threats in the Internet. This paper aims to address two major limitations of current bot detection systems. First, existing flow-based bot detection approaches ignore structural information of botnets, which lead to false detection. Second, they cannot identify the interactive behavioral patterns among heterogeneous botnet objects. In this paper, we propose a novel bot detection framework, namely Bot-AHGCN, which models fine-grained network flow objects (e.g., IP, response) as a multi-attributed heterogeneous graph and transforms bot detection problem into a semi-supervised node classification task on the graph. Particularly, we first build a multi-attributed heterogeneous information network (AHIN) to model the interdependent relationships among botnet objects. Second, we present a weight-learning based node embedding method, which learns the interactive behavioral patterns among bots and integrates them into weighted similarity graphs. Finally, we perform graph convolution on the learned similarity graphs to characterize more comprehensive and discriminative features of bots, and feed them into a forward neural network to identify bots. The overall experimental results on two real-world datasets confirm that Bot-AHGCN outperforms the existing state-of-the-art approaches, and presents better interpretability by introducing meaningful meta-paths and meta-graphs.
- Subjects :
- Information Systems and Management
Artificial neural network
business.industry
Computer science
05 social sciences
Botnet
050301 education
02 engineering and technology
Machine learning
computer.software_genre
Flow network
Computer Science Applications
Theoretical Computer Science
Discriminative model
Artificial Intelligence
Control and Systems Engineering
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
The Internet
Artificial intelligence
business
0503 education
computer
Software
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 537
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........a57e78e89043a8cbd645445925045f2c