Improving antispam techniques by embracing pattern-based filtering

This study attempted to show that there are still away to improve antispam system. The classical method of filtering spam is by inspecting content of an e-mail and finding a matching pattern with a predefined ruleset. Each matched keyword or sentence will produce a weight, also called score, which w...

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Main Author:	Mat Nor, Hairul Anuar
Format:	Thesis
Language:	English
Published:	2009
Subjects:	HE Transportation and Communications QA76 Computer software
Online Access:	http://eprints.utm.my/id/eprint/10003/1/HairulAnuarMatMFC2009.pdf
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id	my-utm-ep.10003
record_format	uketd_dc
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	HE Transportation and Communications QA76 Computer software
spellingShingle	HE Transportation and Communications QA76 Computer software Mat Nor, Hairul Anuar Improving antispam techniques by embracing pattern-based filtering
description	This study attempted to show that there are still away to improve antispam system. The classical method of filtering spam is by inspecting content of an e-mail and finding a matching pattern with a predefined ruleset. Each matched keyword or sentence will produce a weight, also called score, which will be combined to produce the final score and later to be used for identifying spam similarities in the message. Spammers keep changing a style in generating a spam message to avoid being filtered. Bayesian technique was found to be suitable to embed in the antispam in order to recognize the characteristic of spam in a message eventhough the content has been changed. However, the Bayesian introduced difficulties to the system as spammers have changed the way they send the spam specifically to bypass Bayesian filter. Thus, it is time to find a way to filter those spam. Normally an antispam works on the content, but there is a possibility to filter spam based on its pattern of delivering the spam at network level to reduce the congestion in the network. Filter at network level is also benefiting the server as it has eliminated some spam before they are received and processed for the content. A study were conducted to show above statement is true. An Antispam with Pattern Based Filter (ASPBF) will be tested and the result will be compared with the test for Antispam with Bayesian. The comparison result will determine how much it has achieved its objectives. This study will be able to stimulate more studies in the future to further improve antispam solution in the fight against spam and to have a better e-mail communications.
format	Thesis
qualification_level	Master's degree
author	Mat Nor, Hairul Anuar
author_facet	Mat Nor, Hairul Anuar
author_sort	Mat Nor, Hairul Anuar
title	Improving antispam techniques by embracing pattern-based filtering
title_short	Improving antispam techniques by embracing pattern-based filtering
title_full	Improving antispam techniques by embracing pattern-based filtering
title_fullStr	Improving antispam techniques by embracing pattern-based filtering
title_full_unstemmed	Improving antispam techniques by embracing pattern-based filtering
title_sort	improving antispam techniques by embracing pattern-based filtering
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Computer Science and Information System
publishDate	2009
url	http://eprints.utm.my/id/eprint/10003/1/HairulAnuarMatMFC2009.pdf
_version_	1747814760975433728
spelling	my-utm-ep.100032018-10-14T07:21:29Z Improving antispam techniques by embracing pattern-based filtering 2009-04 Mat Nor, Hairul Anuar HE Transportation and Communications QA76 Computer software This study attempted to show that there are still away to improve antispam system. The classical method of filtering spam is by inspecting content of an e-mail and finding a matching pattern with a predefined ruleset. Each matched keyword or sentence will produce a weight, also called score, which will be combined to produce the final score and later to be used for identifying spam similarities in the message. Spammers keep changing a style in generating a spam message to avoid being filtered. Bayesian technique was found to be suitable to embed in the antispam in order to recognize the characteristic of spam in a message eventhough the content has been changed. However, the Bayesian introduced difficulties to the system as spammers have changed the way they send the spam specifically to bypass Bayesian filter. Thus, it is time to find a way to filter those spam. Normally an antispam works on the content, but there is a possibility to filter spam based on its pattern of delivering the spam at network level to reduce the congestion in the network. Filter at network level is also benefiting the server as it has eliminated some spam before they are received and processed for the content. A study were conducted to show above statement is true. An Antispam with Pattern Based Filter (ASPBF) will be tested and the result will be compared with the test for Antispam with Bayesian. The comparison result will determine how much it has achieved its objectives. This study will be able to stimulate more studies in the future to further improve antispam solution in the fight against spam and to have a better e-mail communications. 2009-04 Thesis http://eprints.utm.my/id/eprint/10003/ http://eprints.utm.my/id/eprint/10003/1/HairulAnuarMatMFC2009.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:1289 masters Universiti Teknologi Malaysia Faculty of Computer Science and Information System Bekman, S. (2006). Anti-SPAM Techniques: Bayesian Content Filtering. Retrieved on Mayh 15th 2006, from http://stason.org/articles/technology/e-mail/junkmail/ bayesian_content_filtering.html Foxman, E.R, and Schiano, W.T. (2000). Inspecting Spam Unsolicited Communications on the Internet. Challenges of Information Technology Management in the 21st Century: 2000 Information Resources Management association International Conference. May 21-24, 2000. Anchorage, Alaska. 552. Karlberger, C., Bayler, G., Kruegel, C., and Kirda, E. (2007). Exploting Redundancy in Natural Language to Penetrate Bayesian Spam Filters. FWF Austrian Science Fund. 2007. Kim, H.J, Kim, H.N, Jung, J.J, and Jo, G.S (2004). Spam Mail Filtering System Using Semantic Enrichment. WISE 2004, LNCS 3306. November 22-24, 2004. Brisbane, Australia. 619-628. Liang, G., Li, T., Gong, X., Jiang, Y., Yang, J., and Ni, J. (2006). NASC: A Novel Approach for Spam Classification. ICIC 2006, LNBI 4115. August 16-19, 2006. Kunming, China. 672-681. Mehta, B., Nangia, S., Gupta, M., Nejdl, W. (2008). Detecting Image Spam using Visual Features and Near Duplicate Detection. International World Wide Web Conference Committee (IW3C2). April 21-25 2008, Beijing China. 497-506. McDonald, A. (2004). SpamAssassin: A Practical Guide to Integration and Configuration. Packt Publishing Ltd. Nhung, N.P., and Phuong, T.M. (2007). An Efficient Method for Filtering Image- Based Spam E-mail. Computer Analysis of Images and Patterns: 12th International Conference, CAIP 2007. August 27-29, 2007. Vienna, Autria. 945- 953. Prakash, V.V., and O’Donnel, A. (2005). Fighting Spam with Reputation Systems. Social Computing. Vol. 3 (Issue No 9). ACM. Ramachandran, A. and Feamster, N. (2006). Understanding the Network-Level Behaviour of Spammers. SIGCOMM’06. September 11-15 2006, Pisa Italy. 291-302. Schwartz, A. (2004). SpamAssassin. California. O’Reily. Schultz, B. (2004). TurnTide Stopping Spammers at Their Own Servers. Network World. 2004. Retrieved on April, 26, 2004. From http://www.networkworld.com/nw200/2004/0426watch9.html Xiaochun Cheng, Xiaoqi Ma, Long Wang, and Shaochun Zhong (2005). A Mobile Agent Based Spam Filter System. Computational Intelligence and Security: International Conference, CIS 2005. December 15-19, 2005. Xi’an, China. 422- 427. Zdziarski, J.A. (2005). Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification. San Francisco. No Starch Press, Inc.

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