A Non-genuine Message Detection Method Based on Unstructured Datasets

Marcello Trovati, Richard Hill, Nik Bessis

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

The identification of non-genuine or malicious messages poses a variety of challenges due to the continuous changes in the techniques utilised by cyber-criminals. In this article, we discuss a further evaluation of the text spam recognition method introduced in [1], which is based on semantic properties of documents to assess the level of maliciousness. Further experimental results show the accuracy and potential of our approach.
Original languageEnglish
Title of host publicationProceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages597-600
Number of pages4
ISBN (Print)9781467394734
DOIs
Publication statusPublished - 2015
Externally publishedYes

Publication series

NameProceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015

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    Trovati, M., Hill, R., & Bessis, N. (2015). A Non-genuine Message Detection Method Based on Unstructured Datasets. In Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015 (pp. 597-600). (Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/3PGCIC.2015.108