Employing machine learning techniques for detection and classification of phishing emails

Moradpoor, Naghmeh; Clavie, Benjamin; Buchanan, Bill

Abstract

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.

Citation

Moradpoor, N., Clavie, B., & Buchanan, B. (2017, July). Employing machine learning techniques for detection and classification of phishing emails. Presented at 2017 Computing Conference, London, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2017 Computing Conference
• Start Date: Jul 18, 2017
• End Date: Jul 20, 2017
• Acceptance Date: Nov 11, 2016
• Online Publication Date: Jan 11, 2018
• Publication Date: Jan 11, 2018
• Deposit Date: Jan 12, 2017
• Publicly Available Date: Jan 16, 2017
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: Proceedings of the IEEE Technically Sponsored Computing Conference 2017
• ISBN: 9781509054435
• DOI: https://doi.org/10.1109/SAI.2017.8252096
• Keywords: Intrusion Detection and Classification, Phishing; Emails, Spam Emails, Machine Learning, Artificial Intelligence,; Neural Networks, Cybersecurity, Cyberattacks, Web Attacks
• Public URL: http://researchrepository.napier.ac.uk/Output/461545
• Contract Date: Jan 12, 2017