Abstract
In order to compare the performance of the malicious URLs detection method, researches used the F-score or other detection accuracy to evaluate, but there are some difficulties in evaluating the URL embedding method used in malicious URLs detection because the detection accuracy is also effect by machine learning or deep learning models and data sets. An evaluation method of URL embedding method that is not affected by other factors is particularly important. In this paper, we proposed an intrinsic evaluation method for URL embedding method that is not affected by machine learning models or deep learning models and data sets. Besides, We analyse some URL embedding methods according to intrinsic and extrinsic methods and offer a guidance in selecting suitable embedding methods in URL by analysing the results.
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
References
Top sites - alexa. https://www.alexa.com/topsites
Urlhaus | malware url exchange. https://urlhaus.abuse.ch/
Chen, Q., Omote, K.: A three-step framework for detecting malicious URLs. In: 2022 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6. IEEE (2022)
Goldberg, Y., Levy, O.: word2vec explained: deriving mikolov et al.’s negative-sampling word-embedding method. CoRR abs/1402.3722 (2014). http://arxiv.org/abs/1402.3722
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., Mikolov, T.: Fasttext. zip: compressing text classification models. arXiv preprint arXiv:1612.03651 (2016)
Kaneko, S., Yamada, A., Sawaya, Y., Thao, T.P., Kubota, A., Omote, K.: Detecting malicious websites by query templates. In: Simion, E., Géraud-Stewart, R. (eds.) Innovative Security Solutions for Information Technology and Communications. LNCS, vol. 12001, pp. 65–77. Springer, Cham (2020)
Ke, G., et al.: Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural. Inf. Process. Syst. 30, 3146–3154 (2017)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Rajaraman, A., Ullman, J.D.: Data Mining, pp. 1–17. Cambridge University Press, Cambridge (2011). https://doi.org/10.1017/CBO9781139058452.002
Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282 (1995). https://doi.org/10.1109/ICDAR.1995.598994
Wang, B., Wang, A., Chen, F., Wang, Y., Kuo, C.C.J.: Evaluating word embedding models: methods and experimental results. APSIPA Trans. Sig. Inf. Process. 8, e19 (2019)
Yuan, H., Yang, Z., Chen, X., Li, Y., Liu, W.: Url2vec: URL modeling with character embeddings for fast and accurate phishing website detection. In: 2018 ISPA/IUCC/BDCloud/SocialCom/SustainCom, pp. 265–272 (2018). https://doi.org/10.1109/BDCloud.2018.00050
Acknowledgements
This work was supported by JSPS KAKENHI Grant Number JP22H03588.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 IFIP International Federation for Information Processing
About this paper
Cite this paper
Chen, Q., Omote, K. (2024). Toward the Establishment of Evaluating URL Embedding Methods Using Intrinsic Evaluator via Malicious URLs Detection. In: Meyer, N., Grocholewska-Czuryło, A. (eds) ICT Systems Security and Privacy Protection. SEC 2023. IFIP Advances in Information and Communication Technology, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-031-56326-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-031-56326-3_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56325-6
Online ISBN: 978-3-031-56326-3
eBook Packages: Computer ScienceComputer Science (R0)
