Simple SMS spam filtering on independent mobile phone

Authors:
M. Taufiq Nuruzzaman;Changmoo Lee;Mohd. Fikri Azli bin Abdullah;Deokjai Choi
Affiliations:
Department of Informatics Engineering, State Islamic University Sunan Kalijaga, Yogyakarta, Indonesia;School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea;Faculty of Information Science and Technology, Multimedia University, Malacca, Malaysia;School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
Venue:
Security and Communication Networks
Year:
2012

Citing 16
Cited 0

ACM president's letter: electronic junk

Communications of the ACM
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Content based SMS spam filtering

Proceedings of the 2006 ACM symposium on Document engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Filtering Short Message Spam of Group Sending Using CAPTCHA

WKDD '08 Proceedings of the First International Workshop on Knowledge Discovery and Data Mining
Spam Filter for Short Messages Using Winnow

ALPIT '08 Proceedings of the 2008 International Conference on Advanced Language Processing and Web Information Technology
An Anti-SMS-Spam Using CAPTCHA

CCCM '08 Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02
A New Spam Short Message Classification

ETCS '09 Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 02
The contribution of stylistic information to content-based mobile spam filtering

ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Sampling of Mass SMS Filtering Algorithm Based on Frequent Time-domain Area

WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
A behavior-based SMS antispam system

IBM Journal of Research and Development
Using evolutionary learning classifiers to do MobileSpam (SMS) filtering

Proceedings of the 13th annual conference on Genetic and evolutionary computation
Contributions to the study of SMS spam filtering: new collection and results

Proceedings of the 11th ACM symposium on Document engineering
Independent and Personal SMS Spam Filtering

CIT '11 Proceedings of the 2011 IEEE 11th International Conference on Computer and Information Technology
SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering

Proceedings of the 12th Workshop on Mobile Computing Systems and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

The amount of Short Message Service (SMS) spam is increasing. Various solutions to filter SMS spam on mobile phones have been proposed. Most of these use Text Classification techniques that consist of training, filtering, and updating processes. However, they require a computer or a large amount of SMS data in advance to filter SMS spam, especially for the training. This increases hardware maintenance and communication costs. Thus, we propose to filter SMS spam on independent mobile phones using Text Classification techniques. The training, filtering, and updating processes are performed on an independent mobile phone. The mobile phone has storage, memory and CPU limitations compared with a computer. As such, we apply a probabilistic Naïve Bayes classifier using word occurrences for screening because of its simplicity and fast performance. Our experiment on an Android mobile phone shows that it can filter SMS spam with reasonable accuracy, minimum storage consumption, and acceptable processing time without support from a computer or using a large amount of SMS data for training. Thus, we conclude that filtering SMS spam can be performed on independent mobile phones. We can reduce the number of word attributes by almost 50% without reducing accuracy significantly, using our usability-based approach. Copyright © 2012 John Wiley & Sons, Ltd.