@inproceedings{5a3a0c8d2987462fa166264cf78b4843,
title = "A Non-genuine Message Detection Method Based on Unstructured Datasets",
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.",
keywords = "big data, data mining, Spam detection, text mining",
author = "Marcello Trovati and Richard Hill and Nik Bessis",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015 ; Conference date: 04-11-2015 Through 06-11-2015",
year = "2015",
doi = "10.1109/3PGCIC.2015.108",
language = "English",
series = "Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "597--600",
editor = "Fabrizio Messina and Fatos Xhafa and Ogiela, {Marek R.} and Leonard Barolli",
booktitle = "Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015",
address = "United States",
}