dc.contributor.author | Ma, Kewei | |
dc.date.accessioned | 2021-04-29T15:00:04Z | |
dc.date.available | 2021-04-29T15:00:04Z | |
dc.date.issued | 2021-04-29T15:00:04Z | |
dc.identifier.uri | http://hdl.handle.net/10222/80444 | |
dc.description.abstract | Phishing attacks are the work of social engineering. They are used to trick users to obtain their sensitive/private information using malicious links, websites, and electronic messages. In this thesis, phishing attack detection is explored using information based on uniform resource locators (URLs) and third-party search engine optimization (SEO) tools. A supervised learning approach is used to detect phishing websites. Evaluations are performed using real-world data and a Decision Tree model, which optimized using the Tree-based Pipeline Optimization Tool (TPOT) via Automated Machine Learning (AutoML). The results obtained are not only better than the state-of-the-art models in the literature, but also achieve a 97% detection rate. To utilize the proposed model, the best-performing pipeline from TPOT is embedded to a web API for future remote access. | en_US |
dc.language.iso | en | en_US |
dc.subject | Phishing Detection | en_US |
dc.subject | Machine Learning | en_US |
dc.title | Exploring Phishing Detection Using Search Engine Optimization and Uniform Resource Locator based Information | en_US |
dc.date.defence | 2021-04-27 | |
dc.contributor.department | Faculty of Computer Science | en_US |
dc.contributor.degree | Master of Computer Science | en_US |
dc.contributor.external-examiner | n/a | en_US |
dc.contributor.graduate-coordinator | Evangelos E. Milios | en_US |
dc.contributor.thesis-reader | Vlado Keselj | en_US |
dc.contributor.thesis-reader | Malcolm Heywood | en_US |
dc.contributor.thesis-supervisor | Nur Zincir-Heywood | en_US |
dc.contributor.thesis-supervisor | Riyad Alshammari | en_US |
dc.contributor.ethics-approval | Not Applicable | en_US |
dc.contributor.manuscripts | Not Applicable | en_US |
dc.contributor.copyright-release | Not Applicable | en_US |