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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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).
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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.",
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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. 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