Malicious Text Identification: Deep Learning from Public Comments and Emails
Uloženo v:
| Název: | Malicious Text Identification: Deep Learning from Public Comments and Emails |
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| Autoři: | Asma Baccouche, Sadaf Ahmed, Daniel Sierra-Sosa, Adel Elmaghraby |
| Zdroj: | Information, Vol 11, Iss 312, p 312 (2020) |
| Informace o vydavateli: | MDPI AG |
| Rok vydání: | 2020 |
| Sbírka: | Directory of Open Access Journals: DOAJ Articles |
| Témata: | spam text filter, text mining, content-based classification, natural language processing, multi-label classification, LSTM, Information technology, T58.5-58.64 |
| Popis: | Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset. |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | English |
| Relation: | https://www.mdpi.com/2078-2489/11/6/312; https://doaj.org/toc/2078-2489; https://doaj.org/article/3bb5b7176d574560be4aface49bc8aa2 |
| DOI: | 10.3390/info11060312 |
| Dostupnost: | https://doi.org/10.3390/info11060312 https://doaj.org/article/3bb5b7176d574560be4aface49bc8aa2 |
| Přístupové číslo: | edsbas.A4F9FAF9 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset. |
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| DOI: | 10.3390/info11060312 |