Data analytics : intelligent anti-phishing techniques based on machine learning

Date
2019
Authors
Baadel, Said
Lu, Joan
Journal Title
Journal ISSN
Volume Title
Publisher
World Scientific Publishing Co. Pte Ltd
Abstract
According to the international body Anti-Phishing Work Group (APWG), phishing activities have skyrocketed in the last few years and more online users are becoming susceptible to phishing attacks and scams. While many online users are vulnerable and naive to the phishing attacks, playing catch-up to the phishers' evolving strategies is not an option. Machine Learning techniques play a significant role in developing effective anti-phishing models. This paper looks at phishing as a classification problem and outlines some of the recent intelligent machine learning techniques (associative classifications, dynamic self-structuring neural network, dynamic rule-induction, etc.) in the literature that is used as anti-phishing models. The purpose of this review is to serve researchers, organisations' managers, computer security experts, lecturers, and students who are interested in understanding phishing and its corresponding intelligent solutions. This will equip individuals with knowledge and skills that may prevent phishing on a wider context within the community. © 2019 World Scientific Publishing Co.
Description
This article is not available at CUD collection. The version of scholarly record of this article is published in Journal of Information and Knowledge Management (2019), available online at: https://doi.org/10.1142/S0219649219500059.
Keywords
Classification, Data mining, Dynamic self-structuring neural network, Intelligent anti-phishing, Machine learning, Data analytics, Learning algorithms, Learning systems, Security of data, Anti-phishing, Anti-phishing work groups, Associative classification, Intelligent machine, Intelligent solutions, Machine learning techniques, Phishing attacks, Self structuring neural networks, Computer crime
Citation
Baadel, S., & Lu, J. (2019). Data analytics: Intelligent anti-phishing techniques based on machine learning. Journal of Information and Knowledge Management, 18(1). https://doi.org/10.1142/S0219649219500059