Abstract
With the development of Internet technology, network security is facing great challenges. Malicious URL detection can defend against attacks such as phishing, spams, and malware implantation. However there are some problems on current malicious URL detection, for example the methods used to extract features are inefficient and hard to adapt to the current complex network environment. To solve these problems, this paper uses the word embedding method based on character embedding as the way of vector embedding to improve the deep convolutional neural network, and designs a malicious URL detection system. Finally, we carry out experiments with the system, the results prove the effectiveness of our system.
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Acknowledgments
This research was financially supported by the National Key Research and Development Plan(2018YFB1004101), Key Lab of Information Network Security, Ministry of Public Security(C19614), Special fund on education and teaching reform of Besti(jy201805), the Fundamental Research Funds for the Central Universities(328201910), China Postdoctoral Science Foundation(2019M650606), 2019 Beijing Common Construction Project-Teaching Reform and Innovation Project for Universities in Beijing, key laboratory of network assessment technology of Institute of Information Engineering, Chinese Academy of Sciences. The authors gratefully acknowledge the anonymous reviewers for their valuable comments.
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Wang, Z., Li, S., Wang, B., Ren, X., Yang, T. (2020). A Malicious URL Detection Model Based on Convolutional Neural Network. In: Xiang, Y., Liu, Z., Li, J. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2020. Communications in Computer and Information Science, vol 1298. Springer, Singapore. https://doi.org/10.1007/978-981-15-9031-3_3
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DOI: https://doi.org/10.1007/978-981-15-9031-3_3
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