PHISHGEM: a mobile game-based learning for phishing awareness

Tinubu, Oreoluwa; Falana, Olorunjube James; Oluwumi, Enoch Okikijesu; Sodiya, Adesina S.; Rufai, Sodiq Ademola

doi:10.1080/23742917.2023.2167276

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Phishing against cryptocurrency targets fell from 4% in the second quarter to 2% in the third quarter [2,3]. Furthermore, phishing websites have been spreading by advertising on social media platforms or sending messages to predetermined victims on social media [4].…”

Section: Introductionmentioning

confidence: 99%

Improved Phishing Attack Detection with Machine Learning: A Comprehensive Evaluation of Classifiers and Features

Kapan,

Sora Gunal

2023

Applied Sciences

View full text Add to dashboard Cite

In phishing attack detection, machine learning-based approaches are more effective than simple blacklisting strategies, as they can adapt to new types of attacks and do not require manual updates. However, for these approaches, the choice of features and classifiers directly influences detection performance. Therefore, in this work, the contributions of various features and classifiers to detecting phishing attacks were thoroughly analyzed to find the best classifier and feature set in terms of different performance metrics including accuracy, precision, recall, F1-score, and classification time. For this purpose, a brand-new phishing dataset was prepared and made publicly available. Using an exhaustive strategy, every combination of the feature groups was fed into various classifiers to detect phishing websites. Two existing benchmark datasets were also used in addition to ours for further analysis. The experimental results revealed that the features based on the uniform resource locator (URL) and hypertext transfer protocol (HTTP), rather than all features, offered the best performance. Also, the decision tree classifier surpassed the others, achieving an F1-score of 0.99 and being one of the fastest classifiers overall.

show abstract

Section: Introductionmentioning

confidence: 99%