Phishing Websites Detection by Using Optimized Stacking Ensemble Model

作     者：Zeyad Ghaleb Al-Mekhlafi Badiea Abdulkarem Mohammed Mohammed Al-Sarem Faisal Saeed Tawfik Al-Hadhrami Mohammad T.Alshammari Abdulrahman Alreshidi Talal Sarheed Alshammari 

作者机构：College of Computer Science and EngineeringUniversity of Ha'ilHa'il81481KSA College of Computer Sciences and EngineeringHodeidah UniversityHodeidah967Yemen College of Computer Science and EngineeringTaibah UniversityAl-Madinah42353KSA Nottingham Trent UniversityMansfieldNG185BHUnited Kingdom 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第41卷第4期

页      面：109-125页

核心收录：

学科分类：0839[工学-网络空间安全] 08[工学] 

基　　金：This research has been funded by the Scientific Research Deanship at University of Ha'il-Saudi Arabia through Project Number RG-20023 

主　　题：Phishing websites ensemble classifiers optimization methods genetic algorithm 

摘      要：Phishing attacks are security attacks that do not affect only individuals’or organizations’websites but may affect Internet of Things(IoT)devices and *** environment is an exposed environment for such *** may use thingbots software for the dispersal of hidden junk emails that are not noticed by *** and deep learning and other methods were used to design detection methods for these ***,there is still a need to enhance detection *** of an ensemble classification method for phishing website(PW)detection is proposed in this study.A Genetic Algo-rithm(GA)was used for the proposed method optimization by tuning several ensemble Machine Learning(ML)methods parameters,including Random Forest(RF),AdaBoost(AB),XGBoost(XGB),Bagging(BA),GradientBoost(GB),and LightGBM(LGBM).These were accomplished by ranking the optimized classi-fiers to pick out the best classifiers as a base for the proposed method.A PW data-set that is made up of 4898 PWs and 6157 legitimate websites(LWs)was used for this study s *** a result,detection accuracy was enhanced and reached 97.16 percent.