Phishing Email Detection Based on Binary Search Feature Selection

Abstract

Phishing has appeared as a critical issue in the cybersecurity domain. Phishers adopt email as one of their major channels of communication to lure potential victims. This paper attempts to detect phishing emails by using binary search feature selection (BSFS) with a Pearson correlation coefficient algorithm as a ranking method. The proposed method utilizes four sets of features from the email subject, the body of the email, hyperlinks, and readability of contents. Overall, 41 features were selected from the aforementioned four dimensions. The result shows that the BSFS method evaluated the accuracy of 97.41% in comparison with SFFS (95.63%) and WFS (95.56%). This exploration shows that the SFFS requires more time to ascertain the optimum features set and the WFS requires the least time; however, the accuracy of WFS is very low in comparison with other algorithms. The significant finding of the experiment is that the BFSF requires the least time to evaluate the best feature set with better accuracy even though few features are removed from the feature corpus.