Classifying Pastebin Content Through the Generation of PasteCC Labeled Dataset
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11734 LNAI, Page: 456-467
2019
- 6Citations
- 18Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85072885093&origin=inward; http://dx.doi.org/10.1007/978-3-030-29859-3_39; https://link.springer.com/10.1007/978-3-030-29859-3_39; https://dx.doi.org/10.1007/978-3-030-29859-3_39; https://link.springer.com/chapter/10.1007/978-3-030-29859-3_39
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know