Architecture of adaptive spam filtering based on machine learning algorithms

Islam, Md. Rafiqul and Zhou, Wanlei 2007, Architecture of adaptive spam filtering based on machine learning algorithms, Lecture notes in computer science, vol. 4494, pp. 458-469.

Attached Files
Name Description MIMEType Size Downloads

Title Architecture of adaptive spam filtering based on machine learning algorithms
Author(s) Islam, Md. Rafiqul
Zhou, Wanlei
Journal name Lecture notes in computer science
Volume number 4494
Start page 458
End page 469
Publisher Springer
Place of publication Berlin, Germany
Publication date 2007
ISSN 0302-9743
1611-3349
Keyword(s) machine learning
spam
SVM
NB
FP
Summary Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy.

Language eng
Field of Research 080303 Computer System Security
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007837

Document type: Journal Article
Collection: School of Engineering and Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 5 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 449 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Mon, 29 Sep 2008, 08:56:40 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.