Abstract
The darknet has been a notorious hub for illegal and criminal activities. One of the major challenges in identifying criminals involved in darknet market transactions is the presence of multi-identity vendors, who operate under different accounts to evade detection. Existing methods have limitations such as incomplete feature characterization, imprecise data annotation, and an inability to analyze across markets. In this paper, we propose a new approach to address these issues. Rich information of traded goods is collected from 21 currently existing English darknet markets as experimental data. Our method entails extracting text and image features and computing the writing style of suppliers. Subsequently, the feature dimension is reduced and pseudo labeling is applied to improve label accuracy. Further, four types of data about vendors are mapped onto a heterogeneous information network to characterize potential relationships among darknet vendors. By leveraging the graph neural network algorithm and Siamese neural networks, the link relationship between different accounts is predicted to determine whether they belong to the same supplier. The experimental results demonstrate the effectiveness of our proposed method in identifying different accounts belonging to the same supplier with a maximum accuracy of 99.75% and a recall rate of 99.09%.
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Acknowledgment
This work is supported by the National Key Research and Development Program of China (No.2020YFB1807500)
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Zou, F., Hu, Y., Xu, W., Wu, Y. (2023). Link Prediction-Based Multi-Identity Recognition of Darknet Vendors. In: Wang, D., Yung, M., Liu, Z., Chen, X. (eds) Information and Communications Security. ICICS 2023. Lecture Notes in Computer Science, vol 14252. Springer, Singapore. https://doi.org/10.1007/978-981-99-7356-9_19
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DOI: https://doi.org/10.1007/978-981-99-7356-9_19
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