Title
Deobfuscating leetspeak with deep learning to improve spam filteringPublication Date
2023Other institutions
Instituto Universitário de Lisboa (Iscte)Universidade de Vigo
Instituto de Investigación Sanitaria Galicia Sur (IISGS)
Version
Published versionDocument type
Journal ArticleJournal ArticleLanguage
EnglishRights
© 2023 UNIRAccess
Open accessPublisher’s version
https://doi.org/10.9781/ijimai.2023.07.003Published at
International Journal of Interactive Multimedia and Artificial Intelligence Vol. 8. N. 4. Pp. 46-55Publisher
UNIR - Universidad Internacional de La RiojaKeywords
Convolutional Neural Networks
Deep Learning
Leetspeak
ODS 9 Industria, innovación e infraestructura ... [+]
Deep Learning
Leetspeak
ODS 9 Industria, innovación e infraestructura ... [+]
Convolutional Neural Networks
Deep Learning
Leetspeak
ODS 9 Industria, innovación e infraestructura
Spam Filtering
Text Deobfuscation [-]
Deep Learning
Leetspeak
ODS 9 Industria, innovación e infraestructura
Spam Filtering
Text Deobfuscation [-]
Abstract
The evolution of anti-spam filters has forced spammers to make greater efforts to bypass filters in order to distribute content over networks. The distribution of content encoded in images or the use ... [+]
The evolution of anti-spam filters has forced spammers to make greater efforts to bypass filters in order to distribute content over networks. The distribution of content encoded in images or the use of Leetspeak are concrete and clear examples of techniques currently used to bypass filters. Despite the importance of dealing with these problems, the number of studies to solve them is quite small, and the reported performance is very limited. This study reviews the work done so far (very rudimentary) for Leetspeak deobfuscation and proposes a new technique based on using neural networks for decoding purposes. In addition, we distribute an image database specifically created for training Leetspeak decoding models. We have also created and made available four different corpora to analyse the performance of Leetspeak decoding schemes. Using these corpora, we have experimentally evaluated our neural network approach for decoding Leetspeak. The results obtained have shown the usefulness of the proposed model for addressing the deobfuscation of Leetspeak character sequences. [-]
Collections
- Articles - Engineering [757]
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