A Non-genuine Message Detection Method Based on Unstructured Datasets

Marcello Trovati, Richard Hill, Nik Bessis

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
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

Fingerprint

Semantics

Cite this

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
Trovati, Marcello ; Hill, Richard ; Bessis, Nik. / A Non-genuine Message Detection Method Based on Unstructured Datasets. Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 597-600 (Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015).
@inbook{83673440c14b4ee8a32ae613a9a2668c,
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 = "Spam detection, big data, data mining, text mining",
author = "Marcello Trovati and Richard Hill and Nik Bessis",
year = "2015",
doi = "10.1109/3PGCIC.2015.108",
language = "English",
isbn = "9781467394734",
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",
booktitle = "Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015",

}

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. Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015, Institute of Electrical and Electronics Engineers Inc., pp. 597-600. https://doi.org/10.1109/3PGCIC.2015.108

A Non-genuine Message Detection Method Based on Unstructured Datasets. / Trovati, Marcello; Hill, Richard; Bessis, Nik.

Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 597-600 (Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015).

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - A Non-genuine Message Detection Method Based on Unstructured Datasets

AU - Trovati, Marcello

AU - Hill, Richard

AU - Bessis, Nik

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Spam detection

KW - big data

KW - data mining

KW - text mining

U2 - 10.1109/3PGCIC.2015.108

DO - 10.1109/3PGCIC.2015.108

M3 - Chapter

SN - 9781467394734

T3 - Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015

SP - 597

EP - 600

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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Trovati M, Hill R, Bessis N. 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. Institute of Electrical and Electronics Engineers Inc. 2015. p. 597-600. (Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015). https://doi.org/10.1109/3PGCIC.2015.108