Employing machine learning techniques for detection and classification of phishing emails

• Published in: 2017 Computing Conference : 18-20 July 2017 pp. 149 - 156
• Main Authors: Moradpoor, Naghmeh, Clavie, Benjamin, Buchanan, Bill
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Artificial Intelligence; Cyberattacks; Cybersecurity; Electronic mail; Feature extraction; Intrusion Detection and Classification; Machine Learning; Maximum likelihood estimation; Neural Networks; Phishing Emails; Proposals; Spam Emails; Testing; Training; Web Attacks
• DOI: 10.1109/SAI.2017.8252096

A phishing email is a legitimate-looking email which is designed to fool the recipient into believing that it is a genuine email, and either reveals sensitive information or downloads malicious software through clicking on malicious links contained in the body of the email. Given that phishing emails cost UK consumers £174m in 2015, this paper proposal is driven by a problem whose resolution will have a great impact on people's lives in the UK and in the world. In this paper, we proposed a Neural Network (NN)-based model for detections and classifications of phishing emails using publically available email datasets for both benign and phishing emails. The results of the experiments are presented in order to demonstrate the effectiveness of the model in terms of accuracy, true-positive rate, false-positive rate, network performance and error histogram.