  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, Na\"ive 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.
