An Efficient Spam Mail Detection by Counter Technique

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.




References:
[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.