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2019 | OriginalPaper | Chapter

Classifying Malicious URLs Using Gated Recurrent Neural Networks

Authors : Jingling Zhao, Nan Wang, Qian Ma, Zishuai Cheng

Published in: Innovative Mobile and Internet Services in Ubiquitous Computing

Publisher: Springer International Publishing

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Abstract

The past decade has witnessed a rapidly developing Internet, which consequently brings about devastating web attacks of various types. The popularity of automated web attack tools also pushes the need for better methods to proactively detect the huge amounts of evolutionary web attacks. In this work, large quantities of URLs were used for detecting web attacks using machine learning models. Based on the dataset and feature selection methods of [1], multi-classification of six types of URLs was explored using the random forest method, which was later compared against the gated recurrent neural networks. Even without the need of manual feature creation, the gated recurrent neural networks consistently outperformed the random forest method with well-selected features. Therefore, we determine it is an efficient and adaptive proactive detection system, which is more advanced in the ever-changing cyberspace environment.

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Literature
1.
Cui, B., et al.: Malicious URL detection with feature extraction based on machine learning
2.
Cleary, G., Corpin, M., et al.: Symantec internet security threat report 2017. Symantec Corp., Mountain View, CA, USA, Technical report (2018)
3.
Yang, J., et al.: Multi-classification for malicious URL based on improved semi-supervised algorithm. In: 2017 IEEE International Conference on Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), vol. 1. IEEE (2017)
4.
Zhang, J., Porras, P.A., Ullrich, J.: Highly predictive blacklisting. In: USENIX Security Symposium (2008)
5.
Prakash, P., et al.: PhishNet: predictive blacklisting to detect phishing attacks. In: INFOCOM, 2010 Proceedings IEEE. IEEE (2010)
6.
Hegarty, R., Haggerty, J.: Extrusion detection of illegal files in cloud-based systems. Int. J. Space Based Situated Comput. 5(3), 150–158 (2015)CrossRef
7.
Garera, S., et al.: A framework for detection and measurement of phishing attacks. In: Proceedings of the 2007 ACM Workshop on Recurring Malcode. ACM (2007)
8.
Bahnsen, A.C., et al.: Classifying phishing URLs using recurrent neural networks. In: 2017 APWG Symposium on Electronic Crime Research (eCrime). IEEE (2017)
9.
Wu, Q., et al.: ForesTexter: an efficient random forest algorithm for imbalanced text categorization. Knowl. Based Syst. 67, 105–116 (2014)CrossRef
10.
Dietterich, T.G.: Machine learning for sequential data: a review. In: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). Springer, Heidelberg (2002)
11.
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRef
12.
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12, 850–855 (1999)
13.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
14.
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
15.
Zhao, R., et al.: Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Trans. Ind. Electron. 65(2), 1539–1548 (2018)CrossRef
Metadata
Title
Classifying Malicious URLs Using Gated Recurrent Neural Networks
Authors
Jingling Zhao
Nan Wang
Qian Ma
Zishuai Cheng
Copyright Year
2019
Book
Innovative Mobile and Internet Services in Ubiquitous Computing

Print ISBN: 978-3-319-93553-9

Electronic ISBN: 978-3-319-93554-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-93554-6_36

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