A Review on Mobile SMS Spam Filtering Techniques

Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges, and future research directions on spam detection techniques, filtering, and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analyzed. The most popular techniques for SMS spam detection, filtering, and mitigation are compared, including the used data sets, their findings, and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement.

[1]  Denis Regaud Commission Nationale de l'Informatique et des Libertés , 2016 .

[2]  Hae-Chang Rim,et al.  Korean Mobile Spam Filtering System Considering Characteristics of Text Messages , 2010 .

[3]  René Cumplido,et al.  The Evaluation of Ordered Features for SMS Spam Filtering , 2014, CIARP.

[4]  Emir Crowne,et al.  Canada’s Anti-Spam Legislation: A Constitutional Analysis, 31 J. Marshall J. Info. Tech. & Privacy L. 1 (2014) , 2014 .

[5]  Davar Giveki,et al.  Automatic Detection of Diabetes Diagnosis using Feature Weighted Support Vector Machines based on Mutual Information and Modified Cuckoo Search , 2012, ArXiv.

[6]  Deokjai Choi,et al.  Simple SMS spam filtering on independent mobile phone , 2012, Secur. Commun. Networks.

[7]  Tadas Limba,et al.  Holistic electronic government services integration model , 2014 .

[8]  Akebo Yamakami,et al.  On the Validity of a New SMS Spam Collection , 2012, 2012 11th International Conference on Machine Learning and Applications.

[9]  El-Sayed M. El-Alfy,et al.  Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm , 2016, Future Gener. Comput. Syst..

[10]  Chih-Hung Wu,et al.  A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy , 2007, Expert Syst. Appl..

[11]  Donghai Guan,et al.  Semi-supervised learning using frequent itemset and ensemble learning for SMS classification , 2015, Expert Syst. Appl..

[12]  Xin-She Yang,et al.  Cuckoo search: recent advances and applications , 2013, Neural Computing and Applications.

[13]  Oluwafemi Osho,et al.  Frameworks for mitigating identity theft and spamming through bulk messaging , 2014, 2014 IEEE 6th International Conference on Adaptive Science & Technology (ICAST).

[14]  Huaxiang Zhang,et al.  Analysis on the content features and their correlation of web pages for spam detection , 2015 .

[15]  Roger Piqueras Jover,et al.  Analysis of SMS Spam in Mobility Networks , 2013 .

[16]  Jianfeng Ma,et al.  Content Based Spam Text Classification: An Empirical Comparison between English and Chinese , 2013, 2013 5th International Conference on Intelligent Networking and Collaborative Systems.

[17]  Jung-Tae Lee,et al.  The Contribution of Stylistic Information to Content-based Mobile Spam Filtering , 2009, ACL.

[18]  I. Androulidakis,et al.  Spam goes mobile: Filtering unsolicited SMS traffic , 2012, 2012 20th Telecommunications Forum (TELFOR).

[19]  Oludayo O. Olugbara,et al.  Filtering of Mobile Short Messaging Service Communication Using Latent Dirichlet Allocation with Social Network Analysis , 2014 .

[20]  Claire Cardie,et al.  Negative Deceptive Opinion Spam , 2013, NAACL.

[21]  Shafii Muhammad Abdulhamid,et al.  A Survey of League Championship Algorithm: Prospects and Challenges , 2016, ArXiv.

[22]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[23]  José María Gómez Hidalgo,et al.  Short Messages Spam Filtering Using Personality Recognition , 2016, CERI.

[24]  Michael B. de Leeuw,et al.  Spam After Can-Spam: How Inconsistent Thinking Has Made a Hash out of Unsolicted Commercial E-Mail Policy , 2004 .

[25]  Claire Cardie,et al.  Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.

[26]  Bing Liu,et al.  Review spam detection , 2007, WWW '07.

[27]  Qiang Yang,et al.  SMS Spam Detection Using Noncontent Features , 2012, IEEE Intelligent Systems.

[28]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

[29]  Sarah Jane Delany,et al.  SMS spam filtering: Methods and data , 2012, Expert Syst. Appl..

[30]  Tiago A. Almeida,et al.  Towards SMS Spam Filtering: Results under a New Dataset , 2013 .

[31]  Baris Coskun,et al.  Mitigating SMS spam by online detection of repetitive near-duplicate messages , 2012, 2012 IEEE International Conference on Communications (ICC).

[32]  Sang-Hyun Choi,et al.  SMS Spam Filterinig Using Keyword Frequency Ratio , 2015 .

[33]  Anselm Lambert,et al.  Analysis of Spam , 2003 .

[34]  Giovanni Camponovo,et al.  THE SPAM ISSUE IN MOBILE BUSINESS A COMPARATIVE REGULATORY OVERVIEW , 2004 .

[35]  Muhammad Abulaish,et al.  Graph-based learning model for detection of SMS spam on smart phones , 2012, 2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC).

[36]  Hein S. Venter,et al.  Combating Mobile Spam through Botnet Detection using Artificial Immune Systems , 2012, J. Univers. Comput. Sci..

[37]  Jeng-Shyang Pan,et al.  Cat swarm optimization , 2006 .

[38]  Deokjai Choi,et al.  Independent and Personal SMS Spam Filtering , 2011, 2011 IEEE 11th International Conference on Computer and Information Technology.

[39]  Penny Duquenoy,et al.  Combating Spam through Legislation: A Comparative Analysis of US and European Approaches , 2005, CEAS.

[40]  Juan M. Corchado,et al.  Hybrid learning machines , 2009, Neurocomputing.

[41]  Fabio Massacci,et al.  Using a security requirements engineering methodology in practice: The compliance with the Italian data protection legislation , 2005, Comput. Stand. Interfaces.

[42]  Biju Issac,et al.  Intelligent spam classification for mobile text message , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.

[43]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[44]  Prem Kumar Kalra,et al.  Content-based image classification with wavelet relevance vector machines , 2010, Soft Comput..

[45]  Ingoo Han,et al.  Hybrid genetic algorithms and support vector machines for bankruptcy prediction , 2006, Expert Syst. Appl..

[46]  Christos Faloutsos,et al.  Suspicious Behavior Detection: Current Trends and Future Directions , 2016, IEEE Intelligent Systems.

[47]  Subhajit Basu,et al.  E‐government and developing countries: an overview , 2004 .

[48]  Janez Brest,et al.  A Brief Review of Nature-Inspired Algorithms for Optimization , 2013, ArXiv.

[49]  Robert M. Nishikawa,et al.  Relevance vector machine for automatic detection of clustered microcalcifications , 2005, IEEE Transactions on Medical Imaging.

[50]  Ali A. Ghorbani,et al.  SMS mobile botnet detection using a multi-agent system: research in progress , 2014, ACySE '14.

[51]  JuiHsi Fu,et al.  Detecting spamming activities in a campus network using incremental learning , 2014, J. Netw. Comput. Appl..

[52]  Evelyne Beatrix Cleff,et al.  Privacy Issues in Mobile Advertising , 2007 .

[53]  O. Osho,et al.  Mobile spamming in Nigeria: An empirical survey , 2015, 2015 International Conference on Cyberspace (CYBER-Abuja).

[54]  Ponnurangam Kumaraguru,et al.  Take Control of Your SMSes: Designing an Usable Spam SMS Filtering System , 2012, 2012 IEEE 13th International Conference on Mobile Data Management.

[55]  Qi Xia,et al.  Intelligent spam filtering for massive short message stream , 2013 .

[56]  Patrick Traynor,et al.  Detecting SMS Spam in the Age of Legitimate Bulk Messaging , 2016, WISEC.

[57]  Patrick P. K. Chan,et al.  Spam filtering for short messages in adversarial environment , 2015, Neurocomputing.

[58]  Muhammad Shafie Abd Latiff,et al.  Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm , 2016, PloS one.

[59]  Liang Chen,et al.  TruSMS: A trustworthy SMS spam control system based on trust management , 2015, Future Gener. Comput. Syst..

[60]  Lili Liu,et al.  A Magnetotactic Bacteria Algorithm Based on Power Spectrum for Optimization , 2014, ICSI.

[61]  Akebo Yamakami,et al.  Contributions to the study of SMS spam filtering: new collection and results , 2011, DocEng '11.

[62]  Haruna Chiroma,et al.  A Review of the Applications of Bio-inspired Flower Pollination Algorithm , 2015, SCSE.

[63]  Simon Fong,et al.  Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications , 2011, NDT.

[64]  Muddassar Farooq,et al.  Using evolutionary learning classifiers to do MobileSpam (SMS) filtering , 2011, GECCO '11.

[65]  Waddah Waheeb,et al.  The performance of soft computing techniques on content-based SMS spam filtering , 2015 .

[66]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[67]  Muhammad Khurram Khan,et al.  Application of evolutionary algorithms in detecting SMS spam at access layer , 2011, GECCO '11.

[68]  Tiago A. Almeida,et al.  Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering , 2016, Knowl. Based Syst..

[69]  Tarek M. Mahmoud,et al.  SMS Spam Filtering Technique Based on Artificial Immune System , 2012 .

[70]  Toshihiko Yamakami,et al.  Impact from Mobile SPAM Mail on Mobile Internet Services , 2003, ISPA.

[71]  Li Xiao,et al.  An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm , 2002 .

[72]  Ann E. Skudlark Characterizing SMS spam in a large cellular network via mining victim spam reports , 2014 .

[73]  Hein S. Venter,et al.  Detecting Mobile Spam Botnets Using Artificial immune Systems , 2011, IFIP Int. Conf. Digital Forensics.

[74]  Suku Nair,et al.  Feature Reduction for Optimum SMS Spam Filtering Using Domain Knowledge , 2013 .

[75]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[76]  J. Ioannidis,et al.  The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration , 2009, Annals of Internal Medicine [serial online].

[77]  Syed Hamid Hussain Madni,et al.  An Appraisal of Meta-Heuristic Resource Allocation Techniques for IaaS Cloud , 2016 .

[78]  Mohd Faizal Abdollah,et al.  A Framework for SMS Spam and Phishing Detection in Malay Language: a Case Study , 2014 .

[79]  O. B Longe A Prototype Scalable System for Secured Bulk SMS Delivery on Mobile Networks , 2012 .

[80]  Hsuan-Yi Chou,et al.  Effects of SMS teaser ads on product curiosity , 2014, Int. J. Mob. Commun..

[81]  Guihua Nie,et al.  Ontology-based spam detection filtering system , 2011, 2011 International Conference on Business Management and Electronic Information.

[82]  José María Gómez Hidalgo,et al.  Content based SMS spam filtering , 2006, DocEng '06.

[83]  Caroline Tagg,et al.  A corpus linguistics study of SMS text messaging , 2009 .

[84]  Inwhee Joe,et al.  An SMS Spam Filtering System Using Support Vector Machine , 2010, FGIT.

[85]  Ali Husseinzadeh Kashan,et al.  League Championship Algorithm: A New Algorithm for Numerical Function Optimization , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.

[86]  Shafii Muhammad Abdulhamid,et al.  Symbiotic Organism Search optimization based task scheduling in cloud computing environment , 2016, Future Gener. Comput. Syst..

[87]  J. Ioannidis,et al.  The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration , 2009, BMJ : British Medical Journal.

[88]  Md. Rafiqul Islam,et al.  A multi-tier phishing detection and filtering approach , 2013, J. Netw. Comput. Appl..

[89]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[90]  Nan Jiang,et al.  Greystar : Fast and Accurate Detection of SMS Spam Numbers in Large Cellular Networks using Grey Phone Space , 2013 .

[91]  Patrick Traynor,et al.  Sending Out an SMS: Characterizing the Security of the SMS Ecosystem with Public Gateways , 2016, 2016 IEEE Symposium on Security and Privacy (SP).

[92]  Semih Ergin,et al.  The Impact of Feature Extraction and Selection on SMS Spam Filtering , 2013 .

[93]  Lina Zhou,et al.  Improving Static SMS Spam Detection by Using New Content-based Features , 2014, AMCIS.

[94]  Wildrich Fourie,et al.  Choosing the best classifier for the job: Mobile Filtering for the South African Context , 2013 .

[95]  Ji Hua,et al.  Analysis on the content features and their correlation of web pages for spam detection , 2015, China Communications.

[96]  David E. Sorkin Unsolicited Commercial E-Mail and the Telephone Consumer Protection Act of 1991 , 1997 .

[97]  Wildrich Fourie,et al.  Choosing the best classier for the job: Mobile Filtering for the South African Context , 2012 .

[98]  Lisa Hartling,et al.  Problem-based learning in pre-clinical medical education: 22 years of outcome research , 2010, Medical teacher.

[99]  Vinayak S. Naik,et al.  SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering , 2011, HotMobile '11.

[100]  Lei Hu,et al.  Spam Short Messages Detection via Mining Social Networks , 2012, Journal of Computer Science and Technology.

[101]  Alexandros Papanikolaou,et al.  FIMESS: filtering mobile external SMS spam , 2013, BCI '13.

[102]  Buyile Doris Mdluli Online Consumer Protection: an analysis of the nature and extent of online consumer protection by South African legislation , 2014 .

[103]  He Jiang,et al.  Approximate Muscle Guided Beam Search for Three-Index Assignment Problem , 2014, ICSI.

[104]  Chen Wang,et al.  A behavior-based SMS antispam system , 2010, IBM J. Res. Dev..

[105]  S. Ergin,et al.  A novel framework for SMS spam filtering , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[106]  El-Sayed M. El-Alfy,et al.  Dendritic Cell Algorithm for Mobile Phone Spam Filtering , 2015, ANT/SEIT.

[107]  Prateek Saxena,et al.  The curse of 140 characters: evaluating the efficacy of SMS spam detection on android , 2013, SPSM '13.