Classifying Pastebin Content Through the Generation of PasteCC Labeled Dataset

  1. Adrián Riesco 1
  2. Eduardo Fidalgo 12
  3. Al-Nabki, Mhd Wesam 12
  4. Francisco Jáñez-Martino 2
  5. Enrique Alegre 12
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  2. 2 INCIBE (Spanish National Cybersecurity Institute, León)
Libro:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3 978-3-030-29858-6

Año de publicación: 2019

Páginas: 456-467

Congreso: Hybrid Artificial Intelligent Systems (14. 2019. León)

Tipo: Aportación congreso

Resumen

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.