IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 6    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 6, June 2011 Edition
A DKIM based Architecture for Combating Good Word Attack in Statistical Spam Filters
Full Text(PDF, 3000)  PP.  
Kashefa Kowser.K, Saruladha.K, Packiavathy.M
spam filtering, good word attack, DomainKeys Identified Mail (DKIM)
Abuse of E-Mail by unwanted users causes an exponential increase of E-Mails in user mailboxes which is known as Spam. It is an unsolicited commercial E-mail or unsolicited bulk E-Mail produces huge economic loss to large scale organizations due to high network bandwidth consumption and heavy mail server processing overload. Statistical spam filters could be used to categorize incoming E-Mails into legitimate and spam but they are vulnerable to Good Word attack which obfuscates "good words" in spam messages to make it legitimate. This paper attempts for a counterattack strategy to eradicate insertion of good words by proposing architecture of enhanced DKIM (DomainKeys Identified Mail) as a solution. Our experimental result shows that DKIM serves to be the best as it incorporates sender evidence with random values in the E-Mail messages which is critical for the spammers to evade E-Mail filtering process. The misclassification of the spam E-Mail as legitimate E-Mail would reduce the performance of text classifiers. As the number of E-Mail increases, the misclassification percentage decreases by using DKIM.
[1] Enrico Blanzieri, Anton Bryl, “A Survey of Learning-Based Tec niques of Email Spam Filtering”, January 11, 2008.

[2] Fabrizio Sebastiani, “Machine Learning in Automated Text Categorization”, ACM computing surveys, Vol 34, No 1, 2002.

[3] B. Sirisanyalak and O. Sornil, “An artificial immunity-based spam detection system”, Proc. of IEEE Congress on Evolutionary Computation, 2007, pp.3392-3398.

[4] Gregory Lee Wittel , ”Evaluating and Attacking Statistical Spam Filtering Systems”, Thesis, B.S. (University of California, Davis) 2002.

[5] D. Lowd and C. Meek, “Good word attacks on statistical spam filters”, In Proceedings of the 2nd Conference on Email and Anti- Spam, 2005.

[6] Y. Zhou, Z. Jorgensen and M. Inge, “Combating Good Word Attackson Statistical Spam Filters with Multiple Instance Learning”, Proc.of 19th IEEE International Conference on Tools with Artificial Intelligence, 2007, pp.298-305.

[7] Allman, E., Delany, M., and J. Fenton, “DKIM Sender Signing Practices”, Internet Draft, http://www.ietf.org/internetdrafts/draftallman- dkim-ssp-02.txt (work in progress), August, 2006.

[8] Barry Leiba, Jim Fenton, “DomainKeys Identified Mail (DKIM) Using Digital Signatures for Domain Verification”, Journal of Foundations and Trends in Information Retreival, pp. 538-549, January 2008.

[9] Erkut Sinan Ayla Havesan, Ankara, Attila Ozgit, “An Architecture for End-to-End and Inter-Domain Trusted Mail Delivery Service”, Communications of the ACM, pp. 24-33, February 2007.

[10] Ya-Jeng Lin, Shiuhpyng Shieh, Warren W. Lin, “Lightweight, Pollution-Attack Resistant Multicast Authentication Scheme”, ASIAACCS’06, March 21-24, November 2006.

[11] http://www.di-mgt.com.au/rsa_alg.html

[12] http://www.ocean-logic.com/pub/OL_SHA256.pdf

[13] http://dsmc.eap.gr/members/pkitsos/papers/Kitsos_c09.pdf

Untitled Page