Abstract
With the rapid development of Internet, phishing and other frauds are becoming more and more serious. Criminals posing as banks, electricity providers, social networking sites to send fraudulent information to induce users to log on, steal user information, so that the vast numbers of users and financial institutions suffered property and economic losses. How to accurately and effectively identify phishing related Internet risks has been a major concern of the Internet. This paper analyzes the development history of phishing prevention and control, and presents a Borderline-Smote (Synthetic Minority Over-sampling Technique) DBN (Deeping Belief Network) method to detect phishing. The method uses deep learning phishing detection method based on web documents content and other features to improve 1% on the recognition accuracy. Furthermore the paper uses Borderline-Smote to solve the imbalanced data problem in the training of phishing detection, and further improve 2% on the F-value and recall rate.
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Acknowledgments
This work is supported by the National Key Research and Development Program of China (No. 2016QY03D0605), the National Nature Science Foundation of China (Nos. 61672111, 61370069), and Beijing Natural Science Foundation (No. 4162043).
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Zhang, J., Li, X. (2017). Phishing Detection Method Based on Borderline-Smote Deep Belief Network. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10658. Springer, Cham. https://doi.org/10.1007/978-3-319-72395-2_5
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DOI: https://doi.org/10.1007/978-3-319-72395-2_5
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