All Outputs (3)

Phishing website detection from URLs using classical machine learning ANN model (2021)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2021). Phishing website detection from URLs using classical machine learning ANN model. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (Internet), 2, 509-523. https://doi.org/10.1007/978-3-030-90022-9_28

Phishing is a serious form of online fraud made up of spoofed websites that attempt to gain users’ sensitive information by tricking them into believing that they are visiting a legitimate site. Phishing attacks can be detected many ways, including a... Read More about Phishing website detection from URLs using classical machine learning ANN model.

Cost-sensitive meta-learning framework (2021)
Journal Article
Shilbayeh, S., & Vadera, S. (2021). Cost-sensitive meta-learning framework. Journal of Modelling in Management, https://doi.org/10.1108/JM2-03-2021-0065

Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classif... Read More about Cost-sensitive meta-learning framework.

Phishing email detection using Natural Language Processing techniques : a literature survey (2021)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2021). Phishing email detection using Natural Language Processing techniques : a literature survey. Procedia Computer Science, 189, 19-28. https://doi.org/10.1016/j.procs.2021.05.077

Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks... Read More about Phishing email detection using Natural Language Processing techniques : a literature survey.