Abstract
Web spam is an escalating problem that wastes valuable resources, misleads people and can manipulate search engines in achieving undeserved search rankings to promote spam content. Spammers have extensively used Web robots to distribute spam content within Web 2.0 platforms. We referred to these web robots as spambots that are capable of performing human tasks such as registering user accounts as well as browsing and posting content. Conventional content-based and link-based techniques are not effective in detecting and preventing web spambots as their focus is on spam content identification rather than spambot detection. We extend our previous research by proposing two action-based features sets known as action time and action frequency for spambot detection. We evaluate our new framework against a real dataset containing spambots and human users and achieve an average classification accuracy of 94.70%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gyongyi, Z., Garcia-Molina, H.: Web spam taxonomy. In: Proceedings of the 1st International Workshop on Adversarial Information Retrieval on the Web, Chiba, Japan (2005)
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 Cardiff, Wales (2009)
Tan, P.-N., Kumar, V.: Discovery of Web Robot Sessions Based on their Navigational Patterns. Data Mining and Knowledge Discovery 6, 9–35 (2002)
Hayati, P., Chai, K., Potdar, V., Talevski, A.: HoneySpam 2.0: Profiling Web Spambot Behaviour. In: 12th International Conference on Principles of Practise in Multi-Agent Systems, Nagoya, Japan, pp. 335–344 (2009)
von Ahn, L., Blum, M., Hopper, N., Langford, J.: CAPTCHA: Using Hard AI Problems for Security. In: Biham, E. (ed.) EUROCRYPT 2003. LNCS, vol. 2656, pp. 646–646. Springer, Heidelberg (2003)
Mertz, D.: Charming Python: Beat spam using hashcash, (2004), (August 3, 2009) http://www.ibm.com/developerworks/linux/library/l-hashcash.html (Accessed)
Park, K., Pai, V.S., Lee, K.-W., Calo, S.: Securing Web Service by Automatic Robot Detection. In: USENIX 2006 Annual Technical Conference Refereed Paper (2006)
Uemura, T., Ikeda, D., Arimura, H.: Unsupervised Spam Detection by Document Complexity Estimation. In: Discovery Science, pp. 319–331 (2008)
Sarafijanovic, S., Le Boudec, J.-Y.: Artificial Immune System for Collaborative Spam Filtering. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2007), pp. 39–51 (2008)
Ogbuji, U.: Real Web 2.0: Battling Web spam (August 3, 2009) (2008), http://www.ibm.com/developerworks/web/library/wa-realweb10/ (Accessed)
Abram, H., Michael, W.G., Richard, C.H.: Reverse Engineering CAPTCHAs. In: Proceedings of the 2008 15th Working Conference on Reverse Engineering, vol. 00. IEEE Computer Society, Los Alamitos (2008)
Salvatore, J.S., Shlomo, H., Chia-Wei, H., Wei-Jen, L., Olivier, N., Ke, W.: Behavior-based modeling and its application to Email analysis. ACM Trans. Internet Technol. 6, 187–221 (2006)
Le, Z., Jingbo, Z., Tianshun, Y.: An evaluation of statistical spam filtering techniques. ACM Transactions on Asian Language Information Processing (TALIP) 3, 243–269 (2004)
Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Knowledge and Information Systems 1, 5–32 (1999)
Cooley, R., Mobasher, B., Srivastava, J.: Web mining: information and pattern discovery on the World Wide Web. In: Proceedings of Ninth IEEE International Conference on Tools with Artificial Intelligence 1997, pp. 558–567 (1997)
Chang, C., Lin, C.: LIBSVM: a library for support vector machines, S. a. a. Ed (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Rijsbergen, C.J.V.: Information retrieval Butterworths (1979)
Yiqun, L., Rongwei, C., Min, Z., Shaoping, M., Liyun, R.: Identifying web spam with user behavior analysis. In: Proceedings of the 4th international workshop on Adversarial information retrieval on the web, ACM (2008)
Yu, H., Liu, Y., Zhang, M., Ru, L., Ma, S.: Web Spam Identification with User Browsing Graph. Information Retrieval Technology, 38–49 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hayati, P., Chai, K., Potdar, V., Talevski, A. (2010). Behaviour-Based Web Spambot Detection by Utilising Action Time and Action Frequency. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2010. ICCSA 2010. Lecture Notes in Computer Science, vol 6017. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12165-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-12165-4_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12164-7
Online ISBN: 978-3-642-12165-4
eBook Packages: Computer ScienceComputer Science (R0)