PhisherCop: Developing an NLP-Based Automated Tool for Phishing Detection

Abstract

Phishing poses a major security risk to organizations and individuals leading to loss of billions of dollars yearly. While risk communication serves as a tool to mitigate phishing attempts, it is imperative to create automated phishing detection tools. Numerous Natural Language Processing (NLP) approaches have been deployed to tackle phishing. However, phishing attempts continue to increase exponentially, which reiterates the need for more effective approach. To address this, we have developed an anti-phishing tool called; PhisherCop. PhisherCop is built upon Stochastic Gradient Descent classifier (SGD) and Support Vector Classifier (SVC) which showed an average accuracy of 96%, performing better than six other popular classifiers including: Decision Tree, Logistics Regression, Random Forest, Gradient Boosting Classifier, K-Nearest Neighbors and Multinomial Naive Bayes. Our tool is significant in distinguishing between phishing and legitimate content both over emails and text messages.