Visible to the public Phishing detection: A recent intelligent machine learning comparison based on models content and features

TitlePhishing detection: A recent intelligent machine learning comparison based on models content and features
Publication TypeConference Paper
Year of Publication2017
AuthorsAbdelhamid, N., Thabtah, F., Abdel-jaber, H.
Conference Name2017 IEEE International Conference on Intelligence and Security Informatics (ISI)
PublisherIEEE
ISBN Number978-1-5090-6727-5
Keywordsanti-phishing tools, Artificial neural networks, Classification algorithms, Computer crime, computer security, Data analysis, Decision trees, fake Websites, financial assets, Human Behavior, human factors, intelligent machine learning comparison, Internet, learning (artificial intelligence), machine learning, ML predictive models, online attack, online community, phishing, phishing attacks, phishing datasets, Phishing Detection, phishing detection rate, Predictive Metrics, Predictive models, predictive security metrics, pubcrawl, security, Tools, Training, Trusted Computing, trusted Websites, Web sites, Web Threat, World Wide Web
Abstract

In the last decade, numerous fake websites have been developed on the World Wide Web to mimic trusted websites, with the aim of stealing financial assets from users and organizations. This form of online attack is called phishing, and it has cost the online community and the various stakeholders hundreds of million Dollars. Therefore, effective counter measures that can accurately detect phishing are needed. Machine learning (ML) is a popular tool for data analysis and recently has shown promising results in combating phishing when contrasted with classic anti-phishing approaches, including awareness workshops, visualization and legal solutions. This article investigates ML techniques applicability to detect phishing attacks and describes their pros and cons. In particular, different types of ML techniques have been investigated to reveal the suitable options that can serve as anti-phishing tools. More importantly, we experimentally compare large numbers of ML techniques on real phishing datasets and with respect to different metrics. The purpose of the comparison is to reveal the advantages and disadvantages of ML predictive models and to show their actual performance when it comes to phishing attacks. The experimental results show that Covering approach models are more appropriate as anti-phishing solutions, especially for novice users, because of their simple yet effective knowledge bases in addition to their good phishing detection rate.

URLhttp://ieeexplore.ieee.org/document/8004877/
DOI10.1109/ISI.2017.8004877
Citation Keyabdelhamid_phishing_2017