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Image Spam Detection through Server-Client Filtering by Tracing the Source IP of the Spammer
Abstract
Image spam is a type of e-mail spam that embeds
spam text content into graphical images to bypass traditional textbased e-mail spam filters. To effectively detect image spam, it is desirable to leverage image content analysis technologies. A solution to determine spam images is to embed both server-side filtering and client-side detection. On the server-side, spectral clustering algorithm
is used and in the client-side detection, active learning principle is used. By using spectral clustering algorithm on the server side, it will cluster the spam images and filter the attack activities of spammers
and fast trace back the spam source. By using active learning on client-side filtering, the learner guides the users to label as few images as possible while maximizing the classification accuracy. The
server-side filtering identifies large image clusters as suspicious spam
sources and filtering method is performed to identify the real sources
and block them from beginning.
Keywords
References
M. Ciampa : Security + Guide to Network Security Fundamentals
(second ed. Thomson Publications, Canada 2005).
M.Ciampa: Security + Guide to Network Security Fundamentals (third
ed. Thomson Publications, Canada 2009).
Y. Gao, M. Yang, X. Zhao, B. Pardo, Y.Wu, T. Pappas, and A.
Choudhary,“Image spam hunter,” in Proc. 33th IEEE Int. Conf.
Acoustics, Speech, and Signal Processing, Las Vegas, NV, Apr. 2008.
M. Dredze, R. Gevaryahu, and A. E. Bachrach, Learning Fast Classifiers
for Image Spam, in fourth Conference on Email and Anti-Spam (CEAS
Mountain View, California, (2007).
S. Krasser, T. Yuchun, J. Gould, D. Alperovitch, and P. Judge,
Identifying Image Spam Based on Header and File Properties using C4.5
Decision Trees and Support Vector Machine Learning, in Information
Assurance and Security Workshop, IAW '07.IEEESMC, United States
Military Academy, West Point, New York. (2007), pp. 255-261.
M. Bhaskar, N. Saurabh, G. Manish, and N. Wolfgang, Detecting image
spam using visual features and near duplicate detection, in Proceedings
of the 17th international conference on World WideWeb Beijing, China:
ACM,(2008).
M. Wan, F. Zhang, H.Cheng and Q. Liu ,Text localization in spam
image using edge features, in Proceeding of International Conference on
Communications, Circuits and System, ICCCAS (2008), pp.838-842.
M. Uemura and T. Tabata. Design and Evaluation of a Bayesian-filterbased
Image Spam Filtering Technique, in Proceedings of the 2008
International Conference on Information Security and Assurance,ISA
(2008).
C.Zhang, W.Chen, X.Chen, R.Tiwari, L. Yang and Gary Warner,
Journal of Multimedia, vol.4,no. 5 (2009), pp.313-320.
H.Cheng, Z. Qin,C.Fu and Yong Wang , A novel spam image filtering
framework with Multi-Label Classification, International Conference on
Communications, Circuits and Systems (ICCCAS), (2010),pp.282-285.
T. Liu, W. Tsao, C.Lee, A High Performance Image-Spam Filtering
System, Ninth International Symposium on Distributed Computing and
Applications to Business, Engineering and Science, (2010), pp.445-449.
C. Wang,F. Zhang, F. Li and Q. Liu,Image spam classification based on
low-level image features, in Proceedings of the International Conference
on Communications, Circuits and Systems (ICCCAS), (2010), pp.290-
M.Soranamageswari, C.Meena, A Novel Approach towards Image Spam
Classification, International Journal of Computer Theory and
Engineering vol. 3, no. 1,(2011), pp. 84-88.
D. J. C. MacKay, “Information-based objective functions for active data
selection,” Neural Comput., vol. 4, pp. 590–604, oct 1992.
J. Canny, “A computational approach to edge detection,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 8, no. 6, pp. 679–698, Nov. 1986.
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