Modeling Suspicious Email Detection using Enhanced Feature Selection
This work addresses email security for counter-terrorism efforts, but it is incremental as it builds on known algorithms with a specific feature selection scheme.
The paper tackled suspicious email detection by evaluating machine learning algorithms with enhanced feature selection, achieving improved performance over existing methods.
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.