SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Anti-spam legislation: An analysis of laws and their effectiveness
Information and Communications Technology Law
An Anti-SMS-Spam Using CAPTCHA
CCCM '08 Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02
Email Spam Filtering: A Systematic Review
Foundations and Trends in Information Retrieval
Independent and Personal SMS Spam Filtering
CIT '11 Proceedings of the 2011 IEEE 11th International Conference on Computer and Information Technology
Review: SMS spam filtering: Methods and data
Expert Systems with Applications: An International Journal
Mobile Phone Security and Forensics: A Practical Approach
Mobile Phone Security and Forensics: A Practical Approach
Take Control of Your SMSes: Designing an Usable Spam SMS Filtering System
MDM '12 Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (mdm 2012)
On the Validity of a New SMS Spam Collection
ICMLA '12 Proceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 02
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The widespread use of mobile devices has attracted the attention of cyber-criminals, who exploit their functionality for malevolent purposes. A very popular and well-known such approach is the use of unsolicited electronic messages, also known as spam. Such messages can be used by attackers in order to tempt the recipient to visit a malicious page, or to reply to a message and be charged at premium rates, or even for advertising goods and offers. Several of the mechanisms developed for fighting mobile spam have been based on the well-known and widely adopted e-mail spam techniques. Mobile spam on the other hand has specific properties, such as limited text size, particular linguistic style with specific abbreviations, also known as "the SMS language" or "textese", etc. Our algorithm, FIMESS (FIltering Mobile External SMS Spam), performs simple, yet effective checks on the message headers so as to classify an SMS as being spam or not. In contrast to linguistic-only approaches of spam detection algorithms, FIMESS is able to utilise the important information in the SMS headers and identify SMS spam messages. Contrary to the email metadata which can easily be manipulated by the spammers, the SMS protocol provides useful information to build more efficient spam filters. The proposed scheme was tested on the Android platform and yielded encouraging results.