A Solution for Fighting Spammer's Resources and Minimizing the Impact of Spam
- 1 Electrical Engineering Department, Faculty of Engineering, South Valley University, Aswan, Egypt
Abstract
Spam or unsolicited emails constitute a major threat to the Internet, the corporations, and the end-users. Statistics show that about 70% - 80% of the emails are spam. There are several techniques that have been implemented to react to the spam on its arrival. These techniques consist in filtering the emails and placing them in the Junk or Spam folders of the users. Regardless of the accuracy of these techniques, they are all passive. In other words, they are like someone is hitting you and you are trying by all the means to protect yourself from these hits without fighting your opponent. As we know the proverbs "The best defense is a good offense" or "Attack is the best form of defense". Thus, we believe that attacking the spammers is the best way to minimize their impact. Spammers send millions of emails to the users for several reasons and usually they include some links or images that direct the user to some web pages or simply to track the users. The proposed idea of attacking the spammers is by building some software to collect these links from the Spam and Junk folders of the users. Then, the software periodically and actively visit these links and the subsequent redirect links as if a user clicks on these links or as if the user open the email containing the tracking link. If this software is used by millions of users (included in the major email providers), then this will act as a storm of Distributed Denial of Service attack on the spammers servers and there bandwidth will be completely consumed by this act. In this case, no human can visit their sites because they will be unavailable. In this paper, we describe this approach and show its effectiveness. In addition, we present an application we have developed that can be used for this reason.
- Z. Li and H. Y. Shen. “SOAP: A Social Network Aided Personalized and Effective Spam Filter to Clean Your E-Mail Box,” Proceedings IEEE INFOCOM, Shanghai, 10-15 April 2011, pp. 1835-1843.
- J. A. Zdziarski, “Ending Spam—Bayesian Content Filtering and the Art of Statistical Language Classification,” 5th edition, No Starch Press, San Francisco, 2005.
- L. Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C. D. Spyropoulos and P. Stamatopoulos, “Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach,” Proceedings of the Workshop on Machine Learning and Textual Information Access, 2000, pp. 1-13.
- M. Sahami, S. Dumais, D. Heckerman and E. Horvitz, “A Bayesian Approach to Filtering Junk Email,” Learning for Text Categorization—Papers from the AAAI Workshop, 1998, pp. 55-62.
- D. Karthika-Renuka, et al., “Spam Classification Based on Supervised Learning Using Machine Learning Techniques,” International Conference on Process Automation, Control and Computing, Coimbatore, 20-22 July 2011, pp. 1-7. doi:10.1109/PACC.2011.5979035
- H. Drucker, D. Wu and V. N. Vapnik, “Support Vector Machines for Spam Categorization,” IEEE Transactions on Neural Networks, Vol. 10, No. 5, 1999, pp. 1048-1054. doi:10.1109/72.788645
- C.-Y. Tseng and M.-S. Chen, “Incremental SVM Model for Spam Detection on Dynamic Email Social Networks,” International Conference on Computational Science and Engineering, Vol. 4, 2009, pp. 128-135.
- S. Suwa, et al., “DNS Resource Record Analysis of URLs in E-Mail Messages for Improving Spam Filtering,” IEEE/IPSJ 11th International Symposium on Applications and the Internet (SAINT), Munich, 18-21 July 2011, pp. 439-444.
- A. Khanal, et al., “Improving the Efficiency of Spam Filtering through Cache Architecture,” 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Istanbul, 24-26 October 2007, pp. 303-309. doi:10.1109/MASCOTS.2007.27
- S. A. Elsagheer-Mohamed, “Combining Objective and Subjective Smart Technqiye for a Better Spam Filtering Solution,” Under Submission.
- X. Carreras and L. Márquez, “Boosting Trees for Antispam Email Filtering,” Proceedings of the International Conference on Recent Advances in Natural Language Processing, 2001, pp. 58-64.
- I. M. Rafiqul, et al., “Dynamic Feature Selection for Spam Filtering Using Support Vector Machine,” 6th IEEE/ACIS International Conference on Computer and Information Science, Melbourne, 11-13 July 2007, pp. 757-762.