NORMA eResearch @NCI Library

Detecting Spear-phishing Attacks using Machine Learning

Yamah, Hanson Shonibare (2022) Detecting Spear-phishing Attacks using Machine Learning. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (1MB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (4MB) | Preview


The threat landscape has become larger as a result of the growing number of internet users and its features, particularly in the usage of email communication, and as a result of this, attacks have increased resulting in the loss of money, reputation, data, and emotional wellbeing of individuals and organizations. These threat actors use phishing as one of their tactics, especially spear-phishing, which has evolved into one of their most successful attack vectors due to its high success rate. The use of social engineering, which takes advantage of the victim's emotional and psychological state to disguise hostile emails as legitimate ones, has allowed this attack method to grow so complex that it is challenging to identify them and protect victims. This study examines spear-phishing detection using traditional and automated techniques, and a novel model was developed to identify spear-phishing emails using Random Forest algorithms, and Ensemble learning on a trained and tested dataset of 3000 emails consisting of 1500 normal emails and 1500 spear-phishing emails. This research also classified the rate of accuracy between both algorithms, from which the Random Forest (RF) algorithm performed the best in detecting spear-phishing emails with a 96.33% accuracy rate.

Item Type: Thesis (Masters)
Salahuddin, Jawad
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Tamara Malone
Date Deposited: 08 May 2023 13:16
Last Modified: 08 May 2023 13:16

Actions (login required)

View Item View Item