By Topic

Possibility Theory-Based Approach to Spam Email Detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Tran, D. ; Univ. of Canberra, Canberra ; Wanli Ma ; Sharma, D. ; Thien Nguyen

Most of current spam email detection systems use keywords in a blacklist to detect spam emails. However these keywords can be written as misspellings, for example "baank", "ba-nk" and "bankk" instead of "bank". Moreover, misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing such misspellings. However it is impossible to predict all possible misspellings for a given keyword to add those to the blacklist. We present a possibility theory-based approach to spam email detection to solve this problem. We consider every keyword in the blacklist along with its misspellings as a fuzzy set and propose a possibility function. This function will be used to calculate a possibility score for an unknown email. Using a proposed if-then rule and this core, we can decide whether or not this unknown email is spam. Experimental results are also presented.

Published in:

Granular Computing, 2007. GRC 2007. IEEE International Conference on

Date of Conference:

2-4 Nov. 2007