Research Article
Persistent Clean-Label Backdoor on Graph-Based Semi-supervised Cybercrime Detection
@INPROCEEDINGS{10.1007/978-3-031-56580-9_16, author={Xiao Yang and Gaolei Li and Meng Han}, title={Persistent Clean-Label Backdoor on Graph-Based Semi-supervised Cybercrime Detection}, proceedings={Digital Forensics and Cyber Crime. 14th EAI International Conference, ICDF2C 2023, New York City, NY, USA, November 30, 2023, Proceedings, Part I}, proceedings_a={ICDF2C}, year={2024}, month={4}, keywords={Cybercrime detection Semi-supervised graph learning Backdoor attacks Entity prediction Clean-label Data poisoning}, doi={10.1007/978-3-031-56580-9_16} }- Xiao Yang
Gaolei Li
Meng Han
Year: 2024
Persistent Clean-Label Backdoor on Graph-Based Semi-supervised Cybercrime Detection
ICDF2C
Springer
DOI: 10.1007/978-3-031-56580-9_16
Abstract
Cybercrime, which involves the use of tactics such as hacking, malware attacks, identity theft, ransomware, and online scams, has emerged as a major concern for public security management recently. To combat massive cybercrime and conduct a clean Internet environment, graph-based semi-supervised cybercrime detection (GSCD) has gained increasing popularity recently for it can model complex relationships between network objects and provide node-level behavior predictions. However, in this paper, we present a novel threat on GSCD, named clean-label backdoor attack on GSCD (CBAG), which may be utilized by attackers to escape cybercrime detection successfully. The CBAG patches node features of unmarked training data with adversarially-perturbed triggers to enforce the well-trained GSCD model to misclassify trigger-embedded crime data as the premeditated result. Extensive experiments on multiple detection models and open-source datasets reveal that the CBAG exhibits effective escape performance and evasiveness.
