Publications

@inproceedings{riesco_classifying_2019,

title = {Classifying Pastebin content through the generation of PasteCC labeled dataset},

author = {Adrián Riesco and Eduardo Fidalgo and Wesam Al-Nabki and Francisco Jáñez-Martino and Enrique Alegre},

url = {https://link.springer.com/chapter/10.1007/978-3-030-29859-3_39},

year  = {2019},

date = {2019-01-01},

booktitle = {Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, León, Spain, September 4–6, 2019, Proceedings 14},

pages = {456–467},

publisher = {Springer International Publishing},

abstract = {This paper presents the PasteCC_17K dataset, containing 17,640 text samples from Pastebin, classified into 15 categories, including 6 potentially illegal ones. The study evaluates different text representation techniques and classifiers, finding that TF-IDF with Logistic Regression offers the best performance, helping authorities detect suspicious content on Pastebin.},

keywords = {Cybercrime Detection, Logistic Regression, machine learning, Pastebin, Text classification, TF-IDF},

pubstate = {published},

tppubtype = {inproceedings}

}