Papers Papers/2020



Authors: Alaa Mohasseb 1 ; Benjamin Aziz 1 and Andreas Kanavos 2

Affiliations: 1 School of Computing, University of Portsmouth, Portsmouth, U.K. ; 2 Computer Engineering and Informatics Department, University of Patras, Patras, Greece

ISBN: 978-989-758-478-7

ISSN: 2184-3252

Keyword(s): Information Retrieval, Spam (SMS) Detection, Risk Assessment, Class Imbalance, Machine Learning.

Abstract: Short Message Service (SMS) constitutes one of the most used communication medium. It has become an integral part of people’s lives and like other communication media, SMS texts have been used for propagating spam messages. Despite the fact that a broad range of spam techniques have been proposed to reduce the frequency of such incidents, many difficulties are still present due to text ambiguity; there, the same words can be used in seemingly similar texts which makes it more difficult to identify spam messages. In this paper, we propose an approach for identifying and classifying spam SMS based on the Syntactical features and patterns of the message. The proposed approach consists of four main parts, namely, SMS Pre-processing, Syntactical Features Extraction and Pattern Formulation, Classification and, Risk Analysis. Experimental results show that the proposed approach achieves a good level of accuracy. In addition, to show the effectiveness of handling class imbalance on the classi fication performance, two additional experiments were conducted using the implementation of the SMOTE algorithm. There, the results depicted that handling class imbalance help in improving identification and classification accuracy. Furthermore, based on the above, a risk model has been proposed that addresses the risk probability and the impact of spam SMS. (More)


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Paper citation in several formats:
Mohasseb, A.; Aziz, B. and Kanavos, A. (2020). SMS Spam Identification and Risk Assessment Evaluations. In Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS, ISBN 978-989-758-478-7 ISSN 2184-3252, pages 417-424. DOI: 10.5220/0010022404170424

author={Alaa Mohasseb. and Benjamin Aziz. and Andreas Kanavos.},
title={SMS Spam Identification and Risk Assessment Evaluations},
booktitle={Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,},


JO - Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,
TI - SMS Spam Identification and Risk Assessment Evaluations
SN - 978-989-758-478-7
IS - 2184-3252
AU - Mohasseb, A.
AU - Aziz, B.
AU - Kanavos, A.
PY - 2020
SP - 417
EP - 424
DO - 10.5220/0010022404170424

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