Abstract
Online notepad services allow users to upload and share free text anonymously. Reviewing Pastebin, one of the most popular online notepad services websites, it is possible to find textual content that could be related to illegal activities, such as leaks of personal information or hyperlinks to multimedia files containing child sexual abuse images or videos. An automatic approach to monitor and to detect these activities in such an active and a dynamic environment could be useful for Law Enforcement Agencies to fight against cybercrime. In this work, we present Pastes Content Classification 17K (PasteCC_17K), a dataset of 17640 textual samples crawled from Pastebin, which are classified in 15 categories, being 6 of them suspicious to be related to illegal ones. We used PasteCC_17K to evaluated two well-known text representation techniques, ensembled with three different supervised approaches to classify the pastes of the Pastebin website. We found that the best performance is achieved ensembling TF-IDF encoding with Logistic Regression obtaining an accuracy of \(98.63\%\). The proposed model could assist the authorities in the detection of suspicious content shared in Pastebin.
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
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
Machine Learning library for Python. (Source: http://scikit-learn.org/stable).
References
Aizawa, A.: An information-theoretic perspective of tf-idf measures. Inf. Process. Manage. 39(1), 45–65 (2003)
Al-Nabki, M.W., Fidalgo, E., Alegre, E., Fernández-Robles, L.: Torank: identifying the most influential suspicious domains in the tor network. Expert Syst. Appl. 123, 212–226 (2019)
Al Nabki, M.W., Fidalgo, E., Alegre, E., de Paz Centeno, I.: Classifying illegal activities on tor network based on web textual contents. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Valencia, Spain, April 2017
Bui, D.D.A., Fiol, G.D., Jonnalagadda, S.: Pdf text classification to leverage information extraction from publication reports. J. Biomed. Inform. 61, 141–148 (2016)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Cox, D.R.: The regression analysis of binary sequences. J. Roy. Stat. Soc. B 20, 215–242 (1958)
Diab, D.M., Hindi, K.: Using differential evolution for fine tuning naïve bayesian classifiers and its application for text classification. Appl. Soft Comput. 54, 183–199 (2016)
Harris, Z.S.: Distributional structure. Word 10(2–3), 146–162 (1954)
Herath, H.: Web information extraction system to sense information leakage. Master’s thesis, University of Moratuwa, Sri Lanka (2003)
Hu, R., Jane Delany, S., Mac Namee, B.: EGAL: exploration guided active learning for TCBR. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS (LNAI), vol. 6176, pp. 156–170. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14274-1_13
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. CoRR abs/1607.01759 (2016)
Lochter, J.V., Zanetti, R.F., Reller, D., Almeida, T.A.: Short text opinion detection using ensemble of classifiers and semantic indexing. Expert Syst. Appl. 62, 243–249 (2016)
Matic, S., Fattori, A., Bruschi, D., Cavallaro, L.: Peering into the muddy waters of pastebin. ERCIM News 90, 16 (2012)
Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., Chi, Y.: Deep keyphrase generation. CoRR abs/1704.06879 (2017)
Mironczuk, M., Protasiewicz, J.: A recent overview of the state-of-the-art elements of text classification. Expert Syst. Appl. 106, 36–54 (2018)
Panchenko, A., Ruppert, E., Faralli, S., Ponzetto, S.P., Biemann, C.: Building a web-scale dependency-parsed corpus from commoncrawl. CoRR abs/1710.01779 (2017)
Perlroth, N.: Hackers breach 53 universities and dump thousands of personal records online. New York Times, New York (2012)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)
Silva, R.M., Almeida, T.A., Yamakami, A.: Mdltext: an efficient and lightweight text classifier. Knowl.-Based Syst. 118, 152–164 (2017)
Stein, R.A., Jaques, P.A., Valiati, J.F.: An analysis of hierarchical text classification using word embeddings. CoRR abs/1809.01771 (2018)
Wu, L., Fisch, A., Chopra, S., Adams, K., Bordes, A., Weston, J.: Starspace: Embed all the things! CoRR abs/1709.03856 (2017)
Zhang, Q., Wang, Y., Gong, Y., Huang, X.: Keyphrase extraction using deep recurrent neural networks on twitter. In: EMNLP (2016)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649–657. Neural Information Processing Systems Foundation, January 2015
Zhu, D., Wong, K.W.: An evaluation study on text categorization using automatically generated labeled dataset. Neurocomputing 249, 321–336 (2017)
Acknowledgements
This research is supported by the INCIBE grant “INCIBEI-2015-27359”, corresponding to the “Ayudas para la Excelencia de los Equipos de Investigación avanzada en ciberseguridad” and also by the framework agreement between the University of León and INCIBE (Spanish National Cybersecurity Institute) under Addendum 22 and 01.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Riesco, A., Fidalgo, E., Al-Nabki, M.W., Jáñez-Martino, F., Alegre, E. (2019). Classifying Pastebin Content Through the Generation of PasteCC Labeled Dataset. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-29859-3_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29858-6
Online ISBN: 978-3-030-29859-3
eBook Packages: Computer ScienceComputer Science (R0)