Spam filtering email classification (SFECM) using gain and graph mining algorithm

M.K. Chae, Abeer Alsadoon, P.W.C. Prasad, A. Elchouemi

Research output: Book chapter/Published conference paperConference paperpeer-review

4 Citations (Scopus)

Abstract

This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context -based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (IEEE-CCWC 2017)
EditorsSatyajit Chakrabarti, Himadri Nath Saha
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-7
Number of pages7
ISBN (Print)9781509042289
DOIs
Publication statusPublished - 01 Mar 2017
Event7th IEEE Annual Computing and Communication Workshop and Conference : IEEE-CCWC 2017 - Hotel Stratosphere, Las Vegas, United States
Duration: 09 Jan 201711 Jan 2017
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7864800 (Conference proceedings)

Conference

Conference7th IEEE Annual Computing and Communication Workshop and Conference
CountryUnited States
CityLas Vegas
Period09/01/1711/01/17
Internet address

Fingerprint Dive into the research topics of 'Spam filtering email classification (SFECM) using gain and graph mining algorithm'. Together they form a unique fingerprint.

Cite this