Machine Learning-Based malicious users’ detection in the VKontakte social network
https://doi.org/10.15514/ISPRAS-2020-32(3)-10
Abstract
This paper presents a machine learning-based approach for detection of malicious users in the largest Russian online social network VKontakte. An exploratory data analysis was conducted to determine the insights and anomalies in a dataset consisted of 42394 malicious and 241035 genuine accounts. Furthermore, a tool for automated collection of the information about malicious accounts in the VKontakte online social network was developed and used for the dataset collection, described in this research. A baseline feature engineering was conducted and the CatBoost classifier was used to build a classification model. The results showed that this model can identify malicious users with an overall 0.91 AUC-score validated with 4-folds cross-validation approach.
About the Author
Denis Igorevich SAMOKHVALOV
National Research University Higher School of Economics
Russian Federation
Master student
Russian Federation
Master student
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Review
For citations:
SAMOKHVALOV D.I. Machine Learning-Based malicious users’ detection in the VKontakte social network. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2020;32(3):109-117. https://doi.org/10.15514/ISPRAS-2020-32(3)-10
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