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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Acknowledgments
This work was supported by National Natural Science Foundation of China (No. U1536122).
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Zhao, J., Wang, N., Ma, Q., Cheng, Z. (2019). Classifying Malicious URLs Using Gated Recurrent Neural Networks. In: Barolli, L., Xhafa, F., Javaid, N., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2018. Advances in Intelligent Systems and Computing, vol 773. Springer, Cham. https://doi.org/10.1007/978-3-319-93554-6_36
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DOI: https://doi.org/10.1007/978-3-319-93554-6_36
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