Abstract
Web bots are automated scripts that perform online tasks like human. Abuse of bot technology poses various threats to the security of websites. Recently, mouse dynamics has been applied to bot detection by analyzing whether recorded mouse operations are consistent with human operational patterns. In this paper, we introduce a deep neural network approach to bot detection. We propose a new representation method for mouse movement data, which converts every mouse movement into an image containing its spatial and kinematic information. This representation method makes it possible to utilize CNN models to automate feature learning from mouse movement data. Experimental results demonstrate that our method is able to detect 96.2% of bots with statistical attack ability while traditional detection methods using hand-crafted features or RNN can only detect less than 30% of them.
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This work is supported by NSFC (Grant No. 61772415).
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Wei, A., Zhao, Y., Cai, Z. (2019). A Deep Learning Approach to Web Bot Detection Using Mouse Behavioral Biometrics. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_43
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DOI: https://doi.org/10.1007/978-3-030-31456-9_43
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