Abstract
In today’s world, methods for real-time web page classification are in need due to the tremendous increase in the number of web pages and Internet usage of the people . To address these problems, in the literature, URL-based methods have been proposed which have advantages in classification speed and computational effectiveness over content-based approaches. This work proposes a CNN-based method using URLs only as input. We extract word-level tokens from the URLs alone, feed them into a word embedding layer and then hyper-tunned CNN layers. Our experiments demonstrate that this method can archive an F1-score of 0.9759 and outperforms many existing methods for a new large dataset.
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References
Qi, X., Davison, B.D.: Web page classification: Features and algorithms, ACM Comput. Surv. 41(2), 121–123 (2009)
Kan, M.-Y.: Web page classification without the web page. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters (WWW Alt. ‘04). Association for Computing Machinery, New York, NY, USA, pp. 262–263 (2004). https://doi.org/10.1145/1013367.1013426
Kan, M.-Y., Oanh, N.T.H.: Fast webpage classification using URL features. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM 2005). Association for Computing Machinery, New York, NY, USA, pp. 325–326 (2005). https://doi.org/10.1145/1099554.1099649
Baykan, E., Henzinger, M., Ludmila, M., Weber, I.: A comprehensive study of features and algorithms for URL-based topic classification. ACM Trans. Web 5(3), Article 15, p. 29 (2011). https://doi.org/10.1145/1993053.1993057
Rajalakshmi, R., Aravindan, C.: Naive Bayes approach for website classification. In: Das, V.V., Thomas, G., Lumban Gaol, F. (eds.) AIM 2011. CCIS, vol. 147, pp. 323–326. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20573-6_55
Rajalakshmi, R.: Identifying health domain URLs using SVM. In: Proceedings of the Third International Symposium on Women in Computing and Informatics (WCI 2015). Association for Computing Machinery, New York, NY, USA, pp. 203–208 (2015). https://doi.org/10.1145/2791405.2791441
Rajalakshmi, R., Aravindan, C.: A Naive Bayes approach for URL classification with supervised feature selection and rejection framework. Comput. Intell. 34, 363–396 (2018). https://doi.org/10.1111/coin.12158
Rajalakshmi, R., Tiwari, H., Patel, J., Kumar, A., Karthik, R.: Design of kids-specific URL classifier using recurrent convolutional neural network. Procedia Comput. Sci. 167, 2124–2131 (2020)
Rajalakshmi, R., Ramraj, S., Ramesh Kannan, R.: Transfer learning approach for identification of malicious domain names. In: Thampi, S.M., Madria, S., Wang, G., Rawat, D.B., Alcaraz Calero, J.M. (eds.) SSCC 2018. CCIS, vol. 969, pp. 656–666. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-5826-5_51
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751, Doha, Qatar. Association for Computational Linguistics (2014)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 (NIPS'15). MIT Press, Cambridge, MA, USA, pp. 649–657 (2015)
Kim, Y., Jernite, Y., Sontag, D., Rush, A.M.: Character-aware neural language models. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016). AAAI Press, pp. 2741–2749 (2016)
https://coccoc.com/en/about-us. Accessed 5 May 2022
Jose, C.-C., Mohammad, T.P.: On the role of text preprocessing in neural network architectures: an evaluation study on text categorization and sentiment analysis. In: Proceedings of the EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 40–46 (2018)
https://github.com/trungtv/pyvi. Accessed 5 May 2022
https://keras.io/api/layers/core_layers/embedding/. Accessed 5 May 2022
Stanford University lecture on machine learning. https://cs230.stanford.edu/section/8/. Accessed 5 May 2022
Hung, P.D., Loan, B.T.: Automatic Vietnamese passport recognition on android phones. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds.) FDSE 2020. CCIS, vol. 1306, pp. 476–485. Springer, Singapore (2020). https://doi.org/10.1007/978-981-33-4370-2_36
Quan, D.V., Hung, P.D.: Application of customized term frequency-inverse document frequency for Vietnamese document classification in place of lemmatization. In: Vasant, P., Zelinka, I., Weber, G.-W. (eds.) ICO 2020. AISC, vol. 1324, pp. 406–417. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68154-8_37
Hung, P.D., Minh, N.C.: Application of fuzzy logic in university suggestion system for Vietnamese high school students. In: Dang, T.K., Küng, J., Takizawa, M., Bui, S.H. (eds.) FDSE 2019. LNCS, vol. 11814, pp. 656–664. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35653-8_44
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Hung, P.D., Hung, N.D., Diep, V.T. (2022). URL Classification Using Convolutional Neural Network for a New Large Dataset. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2022. Lecture Notes in Computer Science, vol 13492. Springer, Cham. https://doi.org/10.1007/978-3-031-16538-2_11
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