A novel method in scam detection and prevention using data mining approaches

Mokhtari, M, Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912 and Haghshenas, A 2008, A novel method in scam detection and prevention using data mining approaches , in: IDMC2008, 11-12 Novemver 2008, Amir Kabir University, Tehran Iran.

PDF - Published Version
Download (264kB) | Preview


‘Scam’ is a fraudulence message by criminal intent sent to internet user mailboxes. Many approaches have been proposed to filter out unsolicited messages known as ‘spam’ from legitimate messages known as ‘ham’. However up to this date no suitable approach has been proposed to detect Scams. Almost all spam filters which use Machine Learning approaches, classify scams as hams when scam messages are more similar to the average ham than spam. But such fraudulence messages can be very harmful to users as many people in the world lose their funds by relying on scam messages. In this paper we use Data Mining techniques for scam detection. Bayesian Classifier, Naïve Bayes and K-Nearest Neighbor which are mostly used in spam detection are experimented and the results are reported. In addition, a new approach in scam detection is proposed. This approach uses K-Nearest Neighbour algorithm with modification to Document Similarity equation. Additionally, classification is not binary as ‘scam’ or ‘not scam’: a Fuzzy Decision is used instead of clear types of classes. Scam messages are successfully detected by applying this approach.

Item Type: Conference or Workshop Item (Paper)
Themes: Media, Digital Technology and the Creative Economy
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Proceedings of IDMC2008
Publisher: GITA
Refereed: Yes
Depositing User: Prof. Mo Saraee
Date Deposited: 09 Nov 2011 12:47
Last Modified: 15 Feb 2022 18:07
URI: http://usir.salford.ac.uk/id/eprint/18932

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)


Downloads per month over past year