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Spam 2.0: The Problem Ahead

  • Vidyasagar Potdar
  • Farida Ridzuan
  • Pedram Hayati
  • Alex Talevski
  • Elham Afsari Yeganeh
  • Nazanin Firuzeh
  • Saeed Sarencheh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6017)

Abstract

Webspam is one of the most challenging problems faced by major search engines in the social computing arena. Spammers exploit weaknesses of major search engine algorithms to get their website in the top 10 search results, which results in higher traffic and increased revenue. The development of web applications where users can contribute content has also increased spam, since many web applications like blogging tools, CMS etc are vulnerable to spam. Spammers have developed targeted bots that can create accounts on such applications, add content and even leave comments automatically. In this paper we introduce the field of webspam, what it refers to, how spambots are designed and propagated, why webspam is becoming a big problem. We then experiment to show how spambots can be identified without using CAPTCHA. We aim to increase the general understanding of the webspam problem which will assist web developers, software engineers and web engineers.

Keywords

Webspam CAPTCHA Spambot anti-spam spambot navigation Spam 2.0 Pligg spam 

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References

  1. 1.
    Hayati, P., Potdar, V.: Evaluation of spam detection and prevention frameworks for email and image spam: a state of art. In: Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services. ACM, Linz (2008)Google Scholar
  2. 2.
    Aaron, W.: Ending spam’s free ride. netWorker 7(2), 18–24 (2003)CrossRefGoogle Scholar
  3. 3.
    Fazlollahi, B.: Strategies for Ecommerce Success, p. 300. IGI Publishing (2002)Google Scholar
  4. 4.
    Chris, K., et al.: Spamalytics: an empirical analysis of spam marketing onversion. In: Proceedings of the 15th ACM conference on Computer and communications security, CM, Alexandria (2008)Google Scholar
  5. 5.
    Moheeb Abu, R., et al.: A multifaceted approach to understanding the botnet phenomenon. In: Proceedings of the 6th ACM SIGCOMM conference on Internetmeasurement. ACM, Rio de Janeiro (2006)Google Scholar
  6. 6.
    Group, e., Spam: By Numbers (June 2003)Google Scholar
  7. 7.
    Neal, L.: Vendors Fight Spam’s Sudden Rise. Computer 40(3), 16–19 (2007)CrossRefGoogle Scholar
  8. 8.
    Nucleus, R.: Spam: The silent ROI Killer. Research Note D59 (2003), http://www.nucleusresearch.com (citied July 14,2009)
  9. 9.
    Rockbridge, A.I.: National Technology Readiness Survey: Summary Report 2005 (2004)Google Scholar
  10. 10.
    Vrhnjak, S., Staff, C.: Spam is a big polluter in more ways than one (2009)Google Scholar
  11. 11.
    Yao, Z., et al.: BotGraph: large scale spamming botnet detection. In: Proceedings of the 6th USENIX symposium on Networked systems design and implementation. USENIX Association, Boston (2009)Google Scholar
  12. 12.
    Husna, H., et al.: Behavior Analysis of Spambotnets. In: 3rd International Conference on Communication Systems Software and Middleware and Workshops, COMSWARE 2008, Bangalore, pp. 246–253 (2008)Google Scholar
  13. 13.
  14. 14.
    Antispam. http://antispam.imp.ch/swinoguri-rbl.txt (cited July 13, 2009)
  15. 15.
    Joewein. (cited July 13, 2009)http://www.joewein.net/dl/bl/dom-bl.txt
  16. 16.
    Juniper, http://www.juniper.net/security/spam/ [cited 13 July 2009]
  17. 17.
    Lowd, D., Meek, C.: Good Word Attacks on Statistical Spam Filters. In: Second Conference on Email and Anti-Spam (CEAS), Palo Alto, CA (2005)Google Scholar
  18. 18.
    Cunningham, P., et al.: A Case-Based Approach to Spam Filtering that Can Track Concept Drift. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, p. 3. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  19. 19.
    Yinglian, X., et al.: Spamming botnets: signatures and characteristics. In: Proceedings of the ACM SIGCOMM 2008 conference on Data communication. ACM, Seattle (2008)Google Scholar
  20. 20.
  21. 21.
    Leiba, B., Borenstein, N.: A multifaceted approach to spam reduction. In: First Conference on Email and Anti-Spam, CEAS (2004)Google Scholar
  22. 22.
    Cobb, S.: The Economics of Spam (2003)Google Scholar
  23. 23.
    Rich, L.L.: Internet Legal Issues: SPAM (1999)Google Scholar
  24. 24.
    Schwartz, E.I.: Spam Wars (2003)Google Scholar
  25. 25.
    Halprin, R.: Dependent CAPTCHAs: Preventing the Relay Attack, 26 (2009)Google Scholar
  26. 26.
    Hayati, P., Potdar, V.: Toward Spam 2.0: An Evaluation of Web 2.0 Anti-Spam Methods. In: 7th IEEE International Conference on Industrial Informatics (INDIN 2009), Cardiff, Wales (2009)Google Scholar
  27. 27.
    Sheffield, M.: ’Flag Spam,’ the Preferred Tool of the Left’s Web Censors (2008), (July 2009), http://newsbusters.org/blogs/matthewsheffield/2008/10/07/flag-spamlatest-tool-censors-left (cited July 14, 2009)
  28. 28.
    userscripts.org. Flagging Content Feature, http://userscripts.org/topics/1362 (cited July 14 2009)
  29. 29.
    Hayati, P., Potdar, V.: Spammer and Hacker, Two Old Friends. In: 3rd IEEE International Conference on Digital Ecosystems and Technologies (DEST 2009), Istanbul, Turkey (2009)Google Scholar
  30. 30.
    Hayati, P., Potdar, V.: Toward Spam 2.0: An Evaluation of Web 2.0 Anti-Spam Methods. In: 7th IEEE International Conference on Industrial Informatics (INDIN 2009), Cardiff, Wales (2009)Google Scholar
  31. 31.
    Hayati, P., Chai, K., Potdar, V., Talevski, A.: Behaviour-based web spambot detection by using Action Time and Action Frequency. In: The 2010 International Conference on Computational Science and Applications. Springer, Heidelberg (2010)Google Scholar
  32. 32.
    Hayati, P., Potdar, V., Chai, K., Talevski, A.: Web Spambot Detection Based on Web Usage Behavior. In: The International Conference on Advanced Information Networking and Applications, AINA 2010 (2010)Google Scholar
  33. 33.
    Ridzuan, F.H., Potdar, V., Talevski, A.: Key Parameters in Identifying Cost of Email Spam. In: The 2010 International Conference on Computational Science and Applications. Springer, Heidelberg (2010)Google Scholar
  34. 34.
    Ridzuan, F.H., Potdar, V., Talevski, A.: Key Parameters in Identifying Cost of Spam 2.0. In: 24th IEEE International Conference on Advanced Information Networking and Applications, AINA 2010 (2010)Google Scholar
  35. 35.
    Sarencheh, S., Potdar, V., Yeganeh, E.A., Firouzeh, N.: Semi-Automatic Information Extraction from Discussion Boards with Applications for Anti-Spam Technology. In: International Conference on Computational Science & its Applications (ICCSA 2010). Springer, Heidelberg (2010)Google Scholar
  36. 36.
    Hayati, P., Chai, K., Potdar, V., Talevski, A.: HoneySpam 2.0: Profiling Web Spambot Behaviour. In: Yang, J.-J., Yokoo, M., Ito, T., Jin, Z., Scerri, P. (eds.) PRIMA 2009. LNCS, vol. 5925, pp. 335–344. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Vidyasagar Potdar
    • 1
  • Farida Ridzuan
    • 1
  • Pedram Hayati
    • 1
  • Alex Talevski
    • 1
  • Elham Afsari Yeganeh
    • 1
  • Nazanin Firuzeh
    • 1
  • Saeed Sarencheh
    • 1
  1. 1.Anti Spam Research Lab, Digital Ecosystems and Business Intelligence Institute Curtin University of TechnologyAustralia

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