ABSTRACT
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.
- Taking on the challenge of mobile messaging abuse. White paper, Airwide Solutions Inc., May 2009.Google Scholar
- mSecurity survey 2010. Technical report, Goode Intelligence, 2010.Google Scholar
- Technical realization of the short message service (SMS). TS 23.040, 3rd Generation Partnership Project (3GPP), September 2010. Release 9.Google Scholar
- The Spamhaus Project. http://www.spamhaus.org/ (accessed 2012-03-31), 2012.Google Scholar
- I. Androulidakis. Mobile Phone Security and Forensics: A Practical Approach. Springer Briefs in Electrical and Computer Engineering. Springer, 2012. Google ScholarDigital Library
- I. Androulidakis and C. Basios. A plain type of mobile attack: Compromise of user's privacy through a simple implementation method. In 3rd International Conference on COMmunication System softWAre and MiddlewaRE (COMSWARE), pages 465--470, Bangalore, India, 7--10 January 2008.Google ScholarCross Ref
- I. Androutsopoulos, J. Koutsias, K. V. Chandrinos, and C. D. Spyropoulos. An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '00, pages 160--167, New York, NY, USA, 2000. ACM. Google ScholarDigital Library
- I. Androutsopoulos, G. Paliouras, and E. Michelakis. Learning to filter unsolicited commercial e-mail technical report. Technical Report 2004/2, NCSR Demokritos, 2006.Google Scholar
- K. Bache and M. Lichman. UCI machine learning repository, 2013.Google Scholar
- M. Bueti. Anti-spam legislation. In ITU, WSIS Thematic Meeting on Cybersecurity, pages 1--62, 2005.Google Scholar
- T. Chen and M.-Y. Kan. Creating a live, public short message service corpus: The NUS SMS corpus. Language Resources and Evaluation, pages 1--37, 2012.Google ScholarCross Ref
- G. V. Cormack. Email spam filtering: A systematic review. Found. Trends Inf. Retr., 1(4):335--455, Apr. 2008. Google ScholarDigital Library
- B. Coskun and P. Giura. Mitigating SMS spam by online detection of repetitive near-duplicate messages. In IEEE International Conference on Communications (ICC), pages 999--1004, Ottawa, ON, Canada, 10--15 June 2012.Google ScholarCross Ref
- S. J. Delany, M. Buckley, and D. Greene. SMS spam filtering: Methods and data. Expert Systems with Applications, 2012. Google ScholarDigital Library
- J. M. G. Hidalgo, T. A. Almeida, and A. Yamakami. On the validity of a new SMS spam collection. In 11th International Conference on Machine Learning and Applications (ICMLA), Boca Raton, FL, USA, 12--15 December 2012. Google ScholarDigital Library
- C. Khemapatapan. Thai-English spam SMS filtering. In 16th Asia-Pacific Conference on Communications (APCC), pages 226--230, Auckland, New Zealand, 31 October -- 3 November 2010.Google ScholarCross Ref
- D. Lowther. GSMA to address spam and fraudulent messaging threats for consumers. {Online} http://www.gsma.com/articles/gsma-to-address-spam-and-fraudulent-messaging-threats-for-consumers/17642 (accessed 2012-03-28), 24 March 2010.Google Scholar
- M. T. Nuruzzaman, C. Lee, and D. Choi. Independent and personal SMS spam filtering. In 11th International Conference on Computer and Information Technology (CIT), pages 429--435, Pafos, Cyprus, 31 August -- 2 September 2011. Google ScholarDigital Library
- M. Z. Rafique and M. Abulaish. Graph-based learning model for detection of SMS spam on smart phones. In 8th International Wireless Communications and Mobile Computing Conference (IWCMC), pages 1046--1051, Limassol, Cyprus, 27--31 August 2012.Google ScholarCross Ref
- G. Schryen. Anti-spam legislation: An analysis of laws and their effectiveness. Information & Communications Technology Law, 16(1):17--32, 2007. Google ScholarDigital Library
- G. Schryen. Spam and its economic significance. In Anti-Spam Measures -- Analysis and Design, pages 7--27. Springer Berlin Heidelberg, 2007.Google Scholar
- M. H. Shirali Shahreza and M. Shirali Shahreza. An anti-SMS-spam using CAPTCHA. In ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM '08), volume 2, pages 318--321, Guangzhou, China, 3--4 August 2008. Google ScholarDigital Library
- A. Sillanpaa. Mobile asset security and how to make money on it. In Seminar on Network Security, Publications in Telecommunications Software and Multimedia TML-C7. Helsinki University of Technology, Telecommunications Software and Multimedia Laboratory, 2001. ISSN 1455-9749.Google Scholar
- K. Yadav, S. K. Saha, P. Kumaraguru, and R. Kumra. Take control of your SMSes: Designing an usable spam SMS filtering system. In IEEE 13th International Conference on Mobile Data Management (MDM), pages 352--355, Bengaluru, Karnataka, India, 23--26 July 2012. Google ScholarDigital Library
Index Terms
- FIMESS: filtering mobile external SMS spam
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