Optimal Design of Hierarchical Spam Filtering Method Based on Greylisting

Article Preview

Abstract:

With the continuous development of network technology, the proliferation of spam has become a more horrible threat to normal e-mail service than ever before. Although current spam filtration technologies may block some obvious spams, they still fail to recognize large numbers of tricky ones. Therefore, we propose a novel hierarchical spam filtering method called CFBG grounded on Graylisting and use three layers of well-connected modified filtration technologies to fill up those disadvantages of past filtration technologies. The conclusive result of our experiments shows that the new method can provide more powerful filtration and less misjudgment rate reliably.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

553-557

Citation:

Online since:

September 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] I Androutsopoulos, J Koutsias, KV Chandrinos, An evaluation of naive Bayesian anti-spam filtering, In 4th PKDD's Work-shop on Machine Learning and Textual Information Access, (2000).

DOI: 10.1145/345508.345569

Google Scholar

[2] CT Wu, KT Cheng, Q Zhu, YL Wu, Using visual features for anti-spam filtering, Image Processing, 2005. ICIP 2005. IEEE International Conference on 11-14 Sept. (2005).

DOI: 10.1109/icip.2005.1530440

Google Scholar

[3] Fulu Li, MH Hsieh, An Empirical Study of Clustering Behavior of Spammers and Group-based Anti-Spam Strategies The 3rd conference on email and anti-spam, (2006).

Google Scholar

[4] Xiu-Li Pang, Yu-Qiang Feng, Wei Jiang, A Spam Filter approach with the Improved Machine Learning Technology, Natural Computation, 2007. ICNC 2007. p.484 – 488.

DOI: 10.1109/icnc.2007.143

Google Scholar

[5] Joshua Goodman, Gordon V. Cormack, David Heckerman, Spam and the ongoing battle for the inbox, Communications of the ACM, Volume 50, Issue 2, pp.24-33 February (2007).

DOI: 10.1145/1216016.1216017

Google Scholar

[6] C Kreibich, C Kanich, K Levchenko, Spamcraft: an inside look at spam campaign orchestration, Proceedings of the 2nd USENIX conference on Large-scale exploits and emergent threats: botnets, spyware, worms, and more, (2009).

Google Scholar

[7] Islam R., Wanlei Zhou, An Innovative Analyser for Email Classification Based on Grey List Analysis, Network and Parallel Computing Workshops, 2007. NPC Workshops. IFIP International Conference on 2007 pp.176-182.

DOI: 10.1109/npc.2007.152

Google Scholar

[8] D Puniškis, R Laurutis, Artificial Intelligence for Greylisting Anti-spam, Electronics and Electrical Engineering, (2008).

Google Scholar