By Topic

Detection of SMS spam messages on mobile phones

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Uysal, A.K. ; Bilgisayar Muhendisligi Bolumu, Anadolu Univ., Eskisehir, Turkey ; Gunal, S. ; Ergin, S. ; Gunal, E.S.

In this study, a novel “SMS spam message filter” utilizing effective feature selection and pattern classification techniques is proposed. The proposed filter detects and filters out SMS spam messages in a smart manner rather than black/white list approaches that require intervention of phone users. In the study, Gini index based approach is preferred as the feature selection method. The feature vectors consisting of the selected discriminative features are then fed into two well-known pattern classifiers, namely Naive Bayes and k-Nearest Neighbor, for recognition process. Furthermore, a mobile application, which exploits the proposed detection scheme, is developed particularly for the mobile phones with Android™ operating system. Thus, SMS spam messages are automatically filtered out without disturbing the phone user. The proposed detection scheme is evaluated on a large SMS message dataset consisting of spam and legitimate messages. The results of the experimental work reveal that the proposed system is considerably successful in filtering SMS spam messages.

Published in:

Signal Processing and Communications Applications Conference (SIU), 2012 20th

Date of Conference:

18-20 April 2012