Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
An Efficient Spam Mail Detection by Counter Technique
Authors: Raheleh Kholghi, Soheil Behnam Roudsari, Alireza Nemaney Pour
Abstract:
Spam mails are unwanted mails sent to large number of users. Spam mails not only consume the network resources, but cause security threats as well. This paper proposes an efficient technique to detect, and to prevent spam mail in the sender side rather than the receiver side. This technique is based on a counter set on the sender server. When a mail is transmitted to the server, the mail server checks the number of the recipients based on its counter policy. The counter policy performed by the mail server is based on some pre-defined criteria. When the number of recipients exceeds the counter policy, the mail server discontinues the rest of the process, and sends a failure mail to sender of the mail; otherwise the mail is transmitted through the network. By using this technique, the usage of network resources such as bandwidth, and memory is preserved. The simulation results in real network show that when the counter is set on the sender side, the time required for spam mail detection is 100 times faster than the time the counter is set on the receiver side, and the network resources are preserved largely compared with other anti-spam mail techniques in the receiver side.Keywords: Anti-spam, Mail server, Sender side, Spam mail
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083501
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1768References:
[1] C. Dhinakaran, Jae Kwang Lee, and D. Nagamalai, "An Empirical Study of Spam and Spam Vulnerable email Accounts," in IEEE Conf. of Future Generation Communication and Networking (FGCN 2007), Jeju, Korea, 2007, pp. 403-413.
[2] B. Agrawal, N. Kumar, and M. Molle, "Controlling spam emails at the routers," in Proc. of the 2005 IEEE International Conf. on Communications (ICC 2005), Seoul, Korea, 2005, pp. 1588-1592.
[3] A. Ramachandran, D. Dagon, and N. Feamster, "Can dns-based blacklists keep up with bots?," The Third Conference on Email and Anti-Spam, July 27-28, 2006, California, USA, 2006.
[4] A.Ramachandran, and N. Feamster, "Understanding the network-level behavior of spammers," In Proc. of SIGCOMM, Pisa, Italy, 2006, pp. 291-302.
[5] A. Ciltik, and T. Gungor, "Time-efficient spam e-mail filtering using n-gram models," Pattern Recognition Letters, vol. 29, no. 1, pp. 19-33, Jan. 2008.
[6] G. Cormack, and A. Bratko, "Batch and online spam filter comparison," In Proc. of CEAS, California, USA, 2006.
[7] I. Androutsopoulos, G. Paliouras, V.Karkaletsis, G. Sakkis, C.D. Spyropoulos, and P. Stamatopoulos, "Learning to filter spam e-mail: A Comparison of a Naïve Bayesian and a Memory-Based Approach," 4 European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-2000), Lyon, France , 2000, pp.1-13..
[8] M. Saiful Islam, S.M. Khaled, K. Farhan, A. Rahman, and M. Rahman, "Modeling Spammer Behavior: Naïve Bayes vs. Artificial Neural Networks," IEEE International Conference on Information and Multimedia Technology (ICIMT 2009), Jeju, Korea, 2009, pp.52-55.
[9] M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz, "A baysian approach to filtering junk e-mail," AAAI Workshop on Learning for Text Categorization WS-98-05, Madison, Wisconsin, 1998, pp. 55-62.
[10] P. Roy, A. Roy, Amrit, and V. Thirani, "A New Approach Towards Text Filtering," 2nd International Conference on Machine Vision (ICMV '09), Dubai, UAE, 2009, pp. 282-285.
[11] S. Jungsuk, D. Inque, M. Eto, Kim C. Hyung, and K. Nakao, "An Empirical Study of Spam: Analyzing Spam Sending Systems and Malicious Web Servers," IEEE/IPSJ 10th Annual International Symposium on Applications and the Internet, Seoul, Korea, 2010, pp.257-260.