doi: 10.7763/IJMO.2012.V2.146
Modeling Suspicious Email Detection Using Enhanced Feature Selection
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Panagiotis Karampelas
2
- 1 at Maersk McKinney Moller Institute, University of Southern, Denmark.
- 2 Department of Hellenic American University, Manchester, NH, USA.
Abstract
The paper presents a suspicious email detection model which incorporates enhanced feature selection. In the paper we proposed the use of feature selection strategies along with classification technique for terrorists email detection. The presented model focuses on the evaluation of machine learning algorithms such as decision tree (ID3), logistic regression, Naïve Bayes (NB), and Support Vector Machine (SVM) for detecting emails containing suspicious content. In the literature, various algorithms achieved good accuracy for the desired task. However, the results achieved by those algorithms can be further improved by using appropriate feature selection mechanisms. We have identified the use of a specific feature selection scheme that improves the performance of the existing algorithms.
Keywords
- Decision tree
- feature selection
- logistic regression
- naive bayes
- SVM
How to Cite
Sarwat Nizamani, Nasrullah Memon, Uffe Kock Wiil, and Panagiotis Karampelas, "Modeling Suspicious Email Detection Using Enhanced Feature Selection," International Journal of Modeling and Optimization, vol. 2, no. 4, pp. 371-377, 2012. https://doi.org/ 10.7763/IJMO.2012.V2.146
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).