A systematic review:Detecting phishing websites using data mining models  

作　　者：Dina Jibat Sarah Jamjoom Qasem Abu Al-Haija Abdallah Qusef 

机构地区：[1]the Department of Business Intelligence Technology,Princess Sumaya University for Technology,Amman 11941,Jordan [2]the Department of Cybersecurity,Princess Sumaya University for Technology,Amman 11941,Jordan [3]the Department of Software Engineering,Princess Sumaya University for Technology,Amman 11941,Jordan

出　　处：《Intelligent and Converged Networks》2023年第4期326-341,共16页智能与融合网络（英文）

摘　　要：As internet technology use is on the rise globally,phishing constitutes a considerable share of the threats that may attack individuals and organizations,leading to significant losses from personal and confidential information to substantial financial losses.Thus,much research has been dedicated in recent years to developing effective and robust mechanisms to enhance the ability to trace illegitimate web pages and to distinguish them from non-phishing sites as accurately as possible.Aiming to conclude whether a universally accepted model can detect phishing attempts with 100%accuracy,we conduct a systematic review of research carried out in 2018-2021 published in well-known journals published by Elsevier,IEEE,Springer,and Emerald.Those researchers studied different Data Mining(DM)algorithms,some of which created a whole new model,while others compared the performance of several algorithms.Some studies combined two or more algorithms to enhance the detection performance.Results reveal that while most algorithms achieve accuracies higher than 90%,only some specific models can achieve 100%accurate results.

关 键 词：PHISHING data mining machine learning ALGORITHM CLASSIFICATION 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]