Abstract
In recent years, in order to cope with spam based attacks, there have been many efforts made towards the clustering of spam emails. During the clustering process, many statistical features (e.g., the size of emails) are used for calculating similarities between spam emails. In many cases, however, some of the features may be redundant or contribute little to the clustering process. Feature selection is one of the most typical methods used to identify a subset of key features from an initial set. In this paper, we propose a heuristic-based feature selection method for clustering spam emails. Unlike the existing methods in that they make the combinations of given features and evaluate them using data mining and machine learning techniques, our method focuses on evaluating each feature according to only its value distribution in spam clusters. With our method, we identified 4 significant features which yielded a clustering accuracy of 86.33% with low time complexity.
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
Zhuang, L., Dunagan, J., Simon, D.R., Wang, H.J., Tygar, J.D.: Characterizing botnets from email spam records. In: Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats, San Francisco, vol. (2), pp. 1–9 (2008)
Li, F., Hsieh, M.H.: An Empirical Study of Clustering Behavior of Spammers and Group-based Anti-Spam Strategies. In: Proceedings of 3rd Conference on Email and Anti-Spam (CEAS), Mountain View, CA, pp. 21–28 (2006)
Xie, Y., Yu, F., Achan, K., Panigrahy, R., Hulten, G., Osipkov, I.: Spamming botnets: signatures and characteristics. ACM SIGCOMM Computer Communication Review 38(4) (October 2008)
Song, J., Inoue, D., Eto, M., Kim, H., Nakao, K.: An Empirical Study of Spam: Analyzing Spam Sending Systems and Malicious Web Servers. In: 10th Annual International Symposium on Applications and the Internet (SAINT 2010), Seoul, Korea, pp. 19–23 (July 2010)
Anderson, D.S., Fleizach, C., Savage, S., Voelker, G.M.: Spamscatter: Characterizing Internet Scam Hosting Infrastructure. In: Proceedings of the USENIX Security Symposium, Boston (2007)
Song, J., Inoue, D., Eto, M., Kim, H., Nakao, K.: O-means: An Optimized Clustering Method for Analyzing Spam Based Attacks. IEICE Transactions on Fundamentals E94-A(1) (January 2010)
Fetterly, D., Manasse, M., Najork, M., Wiener, J.L.: A large-scale study of the evolution of web pages. Softw. Pract. Exper. 34(2) (2004)
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
Song, J., Eto, M., Kim, H.C., Inoue, D., Nakao, K. (2010). A Heuristic-Based Feature Selection Method for Clustering Spam Emails. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-17537-4_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17536-7
Online ISBN: 978-3-642-17537-4
eBook Packages: Computer ScienceComputer Science (R0)