作者
Yazan Ahmad Alsariera, Adeyemo Victor Elijah, Abdullateef O Balogun
发表日期
2020/12
期刊
Arabian Journal for Science and Engineering
卷号
45
期号
12
页码范围
10459-10470
出版商
Springer Berlin Heidelberg
简介
The damaging effect of phishing is traumatizing as attackers or hackers execute theft of sensitive information from users subtly for inappropriate or unauthorized usage. In the light of curbing phishing, blacklisting of websites proved ineffective as the deployment of phishing websites are rampantly increasing and often short-lived. Hence, machine learning (ML) methods are seen as viable measures and used to develop deplorable models that can detect a phishing website. ML methods are fast gaining attention and acceptance in detecting phishing websites as they can cope with the dynamism of phishing websites and attackers. However, ML methods still suffer some shortcomings in terms of low detection accuracy, high false alarm rate (FAR) and induced bias of developed ML solutions. In addition, with the evolving nature of phishing attacks, there is a continuing imperative need for novel and effective ML-based …
学术搜索中的文章
YA Alsariera, AV Elijah, AO Balogun - Arabian Journal for Science and Engineering, 2020