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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Patil, D.R., Patil, J.: Survey on malicious web pages detection techniques. Int. J. u-and e-Serv. Sci. Technol. 8(5), 195–206 (2015)
Yu, B., Pan, J., Hu, J., Nascimento, A., De Cock, M.: Character level based detection of DGA domain names. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)
Choudhary, C., Sivaguru, R., Pereira, M., Yu, B., Nascimento A., De Cock M.: Algorithmically generated domain detection and malware family classification. In: International Symposium on Security in Computing and Communication, pp. 640–655. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-5826-5_50
Garera, S., Provos, N., Chew, M., et al.: A framework for detection and measurement of phishing attacks. In: ACM Workshop on Recurring Malcode. ACM (2007)
Gupta, D., KM, M.: Behind phishing: an examination of phisher modi operandi. In: Usenix Workshop on Large-scale Exploits & Emergent Threats. DBLP (2008)
Ma, J., Saul, L.K, Savage, S., et al.: Beyond blacklists: learning to detect malicious Web sites from suspicious URLs. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28–July 1.ACM (2009)
Choi, H., Zhu, B.B., Lee, H.: Detecting malicious web links and identifying their attack types. In: Proceedings of the 2nd USENIX Conference on Web Application Development (2011)
Bartos, K., Sofka, M., Franc, V.: Optimized invariant representation of network traffic for detecting unseen malware variants. In: USENIX Security Symposium, pp. 807–822 (2016)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, p. 8. Beijing:Posts & Telecom Press, Beijing (2016)
Feng, Z., Shuo, C., Xiaochuan, W.: Classification for DGA-based malicious domain names with deep learning architectures. In: Second International Conference on Applied Mathematics and Information Technology, p. 5 (2017)
Xu, C., Shen, J., Du, X.: Detection method of domain names generated by DGAs based on semantic representation and deep neural network. Comput. Secur. 85, 77–88 (2019)
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP (2014)
Zhang, X., Zhao, J., Lecun, Y.: Character-level Convolutional Networks for Text Classification (2015)
Wen, Z.: Research and design of malicious URL Detection algorithm on Deep learning. Beijing University of Posts and Telecommunications (2019)
Shibahara, T., Yamanishi, K., Takata, Y., et al.: Malicious URL sequence detection using event de-noising convolutional neural network. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–7. IEEE (2017)
Woodbridge, J., Anderson, H.S., Ahuja, A., et al.: Predicting Domain Generation Algorithms with Long Short-Term Memory Networks (2016)
Dhingra, B., Zhou, Z., Fitzpatrick, D., et al.: Tweet2Vec: Character-Based Distributed Representations for Social Media (2016)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-15-9031-3_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9030-6
Online ISBN: 978-981-15-9031-3
eBook Packages: Computer ScienceComputer Science (R0)