International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.8, No.1, Jan. 2016

Study on the Effectiveness of Spam Detection Technologies

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Muhammad Iqbal, Malik Muneeb Abid, Mushtaq Ahmad, Faisal Khurshid

Index Terms

Spam Detection Technologies;Machine Learning;Whitelists and Blacklists Signatures;Spam score


Nowadays, spam has become serious issue for computer security, because it becomes a main source for disseminating threats, including viruses, worms and phishing attacks. Currently, a large volume of received emails are spam. Different approaches to combating these unwanted messages, including challenge response model, whitelisting, blacklisting, email signatures and different machine learning methods, are in place to deal with this issue. These solutions are available for end users but due to dynamic nature of Web, there is no 100% secure systems around the world which can handle this problem. In most of the cases spam detectors use machine learning techniques to filter web traffic. This work focuses on systematically analyzing the strength and weakness of current technologies for spam detection and taxonomy of known approaches is introduced.

Cite This Paper

Muhammad Iqbal, Malik Muneeb Abid, Mushtaq Ahmad, Faisal Khurshid,"Study on the Effectiveness of Spam Detection Technologies", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.1, pp.11-21, 2016. DOI: 10.5815/ijitcs.2016.01.02


[1]D. Saraswathi, A. V. Kathiravan, R. Kavitha. Link Farm Detection using SVMLight Tool. In proceedings of ICCCI 2012 International Conference on computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA.

[2]Eiron, N., Curley, K. S., and Tomlin, J. A. 2004. Ranking the web frontier. In Proceedings of the 13th international conference on World Wide Web. ACM Press, New York, NY, USA, 309–318.

[3]Z. Gyongyi, H. Garcia-Molina, Web Spam Taxonomy. In First International Workshop on Adversarial Information Retrieval on the Web, 2005. 

[4]P. P.K. Chan,C.Yang,D. S. Yeung,W. W.Y. Ng, Spam filtering for short messages in adversarial environment, Neurocomputing, Vol. 155, 1 May 2015, pp. 167–176.

[5]L. Chen, Z. Yan, W. Zhang, R. Kantola, TruSMS: A trustworthy SMS spam control system based on trust management, Future Generation Computer Systems, Vol. 49, August 2015, pp. 77–93.

[6]China: mobile phone subscribers by month February 2015 [online], Available:, June 11, 2015. 

[7]L. Ying-Lien, H. Chih-Hsiang, Usability study of text-based CAPTCHAs, Displays, Vol.32, no. 2, pp. 81–86, April 2011.

[8]H. Beitollahi,G. Deconinck, Analyzing well-known countermeasures against distributed denial of service attacks, Computer Communications ,Vol. 35, no. 11, 15 June 2012,pp. 1312–1332.

[9]J.K. Dharavath, F. A. Talukdar, R. H. Laskar, Study on Biometric Authentication Systems, Challenges and Future Trends: A Review , International Conference on Computational Intelligence and Computing Research (ICCIC), 2013 IEEE Enathi.

[10]S. Heron, Technologies for spam detection, Network Security Vol. 2009, no. 1, January 2009, pp. 11–15.

[11]D. Ruano-Ordás,J. Fdez-Glez,F. Fdez-Riverola, J.R. Méndez, Effective scheduling strategies for boosting performance on rule-based spam filtering frameworks, Journal of Systems and Software ,Vol. 86,no. 12,, December 2013,pp. 3151–3161.

[12]Z. Qingshan , W. Shaobing, C. Ying, J. Xueming, The Research of Information Filtering Technology Based on Bayesian Network, Procedia Environmental Sciences , Vol. 11 (2011) , pp.545 – 551.

[13]X. Du, Internet adoption and usage in China, Proceedings of the 27th   Annual Telecommunications Policy and Research Conference, Alexandria, VA (1999). 

[14]CNNIC 33rd Statistical Report,[online], Available:, June 15,2015.

[15]Spam and Phishing Statistics Report Q1-2014,[online], Available:, June 16, 2015.

[16]2015 Internet Security Threat Report Vol.20,[online], Available:, June 18,2015.

[17]Microsoft Security Intelligence Report Vol. 18, [online], Available:, May 14, 2015.

[18]Cisco 2014 Annual Security Report,[online], Available: May 4, 2015.

[19]N. Eagle, A. Pentland, Social serendipity: mobilizing social software, IEEE Pervas. Comput., Vol. 04-2 (2005), pp. 28–34

[20]D. Evans, The Internet of Things How the Next Evolution of the Internet Is Changing Everything, Cisco Internet Business Solutions Group (IBSG) April 2011[online],Available:, 2 April 2015

[21]N. Spirin, J. Han, Survey on Web Spam Detection: Principles and Algorithms, SIGKDD Explorations, Vol. 13, no. 2, pp. 50-64, 2011

[22]J. Carpinter, R. Hunt, Tightening the net: A review of current and next generation spam filtering tools, Computers & Security, Vol. 25, no. 8, pp. 566–578, November 2006

[23]C. Laorden, X. Ugarte-Pedrero, I.Santos, B. Sanz , J. Nieves, P. G. Bringas, Study on the effectiveness of anomaly detection for spam filtering,  Information Sciences Vol. 277, pp. 421–444, September 2014

[24]T.S. Guzella, W. M. Caminhas, A review of machine learning approaches to Spam filtering, Expert Systems with Applications, Vol.36, no. 7, pp. 10206–10222, September 2009

[25]M. A. Al-Kadhi,  Assessment of the status of spam in the Kingdom of Saudi Arabia,  Journal of King Saud University Computer and Information Sciences, Vol. 23, no. 2,pp. 45–58, July 2011

[26]S. Dinha,T. Azeba,F. Fortinb,D. Mouheba,M. Debbabia, Spam campaign detection, analysis, and investigation, Digital Investigation, Vol. 12 supplement 1, March 2015, Pages S12–S21, March 2015

[27]C. Garrigues, N. Migas ,W. Buchanan,S. Robles,J. Borrell, Protecting mobile agents from external replay attacks, Journal of Systems and Software, Volume 82, Issue 2, pp. 197–206, February 2009

[28]W. Liang, W. Wang, On performance analysis of challenge/response based authentication in wireless networks, Computer Networks Vol. 48,no. 2, pp. 267–288, 6 June 2005

[29]D. Ruano-Ordás, J. Fdez-Glez ,F. Fdez-Riverola ,J.R. Méndez, Effective scheduling strategies for boosting performance on rule-based spam filtering frameworks, Journal of Systems and Software, Vol. 86, no. 12, pp. 3151–3161, December 2013.

[30]Yahoo Research, 2007, Web Spam Collections, [online], Available:, May 10, 2013.

[31]Messaging, Malware and Mobile Anti-Abuse Working Group, Report #16 – 1st Quarter 2012 through 2nd Quarter 2014,[online], Available:, May 1, 2015

[32]G. González-Talaván, A simple, configurable SMTP anti-spam filter: Greylists, Computers & Security, Vol. 25, no. 3, pp. 229–236, May 2006

[33]G. Robinson, A statistical approach to the spam problem, Linux J., 2003 (2003), p. 3

[34]P. hirita, J. Diederich, W. Nejdl, MailRank: using ranking for spam detection,  Proceedings of the 14th  ACM International Conference on Information and Knowledge Management, ACM (2005), pp. 373–380

[35]G. Schryen, A formal approach towards assessing the effectiveness of anti-spam procedures, Proceedings of the 39th Annual Hawaii International Conference on System Sciences, 2006, HICSS’06, IEEE, , pp. 129–138, 2006

[36]L. Zhang, J. Zhu, T. Yao,An evaluation of statistical spam filtering techniques, ACM Trans. Asian Lang. Inform. Process. (TALIP), 3 (2004), pp. 243–269