Design and Implementation of Intelligent Spam Filtering System

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With the rapid development of Internet, E-mail has been widely applied, and along goes a great deal of useless and harmful information. In the face of todays rampant spam developments, anti-spam mechanism is the mail filtering technology has gradually become the focus of information security. While the technical performance of spam filtering is good or bad, the key lies in the amount of spam sample collection, study and analysis. Through the analyzing and processing of spam, the paper designs and implements the intelligent spam filtering system. It brings forward some new theories. Based on analyzing actuality, origin and characteristic of spam, the paper also mainly expounds several filtering technique applied in E-mail.

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Advanced Materials Research (Volumes 846-847)

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1624-1627

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November 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Rachna Dhamija and J.D. Tygar. Phish and HIPs: Human Interactive Proofs to Detect Phishing Attacks. In In Human Interactive Proofs: Second International Workshop. 127-141, (2005).

DOI: 10.1007/11427896_9

Google Scholar

[2] Li Baolip, Chen Yuzhong, Yu Shiwen. A Comparative Study on Automatic Categorization Methods for Chinese Search Engineer[C]. In proceedings of Eighth Joint International Computer Conference. 117-120. (2002).

Google Scholar

[3] C. Dwork and M. Naor. Pricing via Processing or Combatting Junk Mail. In Proceedings of CRYPTO'92, Lecture Notes in Computer Science. 137-147, (1992).

DOI: 10.1007/3-540-48071-4_10

Google Scholar

[4] Joachims T. Text Categorization with support vector machines: Learning with many Relevant Features[C]. In proceedings of the 10th European conference on Machine Learning. (1998).

DOI: 10.1007/bfb0026683

Google Scholar

[5] I. Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C.D. Spyropoulos and P. Stamatopoulos. Learning to Filter Spam Email: A comparison of a Navie Bayesian and a memory-based Approach[C]. In proceedings 4th European conference on principles and practice of knowledge discovery in databases. 1-13. (2000).

DOI: 10.1023/a:1022948414856

Google Scholar

[6] X. Carreras, L. Marquez. Boosting Trees for anti-spam email filtering[C]. In proceedings of Euro Conferences Recent Advances in NLP. 58-64. (2001).

Google Scholar

[7] A. Kolcz, J. Alspeetor. SVM-based Filtering of E-mail Spam with content specific misclassification costs[C]. In proceedings of ICDM-2001 Workshop on text mining. (2001).

Google Scholar

[8] M. Sahami, S. Dumais, D. Heekerman and E. Horvitz.A. Bayesian approach to Filtering Junk EMail[C]. In Proeeedings of AAAI-98 Work shop on Learning for Text Categorization. 55-62. (1998).

Google Scholar

[9] T. Nichoalas. Using Adaboost and decision stumps to identify spam email[EB/OL]. Stanford University course project. http: /nlp. stanford. edu/course/cs224n/2003/fp/tyronen/report. pdf. (2003).

Google Scholar

[10] Z Pawlak. Rough sets[J]. International Journal of Computer and Information Sciences. 11(5): 341-356. (1982).

Google Scholar

[11] Hrishikesh B. Aradhye, Gregory K. Myers, James A. Herson. Image Analysis for Efficient Categorization of Image-based Spam E-mail[J]. Document Analysis and Recognition. 2(9): 914-918. (2005).

DOI: 10.1109/icdar.2005.135

Google Scholar

[12] H. Drucker, D. Wu, V.N. Vapnik. Support Vector Machines for spam categorization[J]. IEEE Transactions on Neural networks. 20(5), 1048-1054. (1999).

DOI: 10.1109/72.788645

Google Scholar