{"title":"使用集成学习的网络钓鱼网站检测","authors":"","doi":"10.30534/ijeter/2023/031112023","DOIUrl":null,"url":null,"abstract":"Phishing is also the most common type of data breach. As a result, it is carried out by sending an email with links that lead to fraudulent websites. This technique is especially targeted to large companies. Usually, the attackers send emails with work-related information. Machine learning is one of the most successful techniques for detecting phishing. This paper analyzed the results of various machine learning techniques for predicting phishing websites. And also describes the various methods that are used to identify phishing websites. Some of these include the SVM classification method, Random Forest method, and AdaBoost method. Ensemble model that combines the SVM, Random Forest, and AdaBoost methods was able to classify a phishing site with an accuracy of 96%","PeriodicalId":13964,"journal":{"name":"International Journal of Emerging Trends in Engineering Research","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Phishing Website Detection Using Ensemble Learning\",\"authors\":\"\",\"doi\":\"10.30534/ijeter/2023/031112023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Phishing is also the most common type of data breach. As a result, it is carried out by sending an email with links that lead to fraudulent websites. This technique is especially targeted to large companies. Usually, the attackers send emails with work-related information. Machine learning is one of the most successful techniques for detecting phishing. This paper analyzed the results of various machine learning techniques for predicting phishing websites. And also describes the various methods that are used to identify phishing websites. Some of these include the SVM classification method, Random Forest method, and AdaBoost method. Ensemble model that combines the SVM, Random Forest, and AdaBoost methods was able to classify a phishing site with an accuracy of 96%\",\"PeriodicalId\":13964,\"journal\":{\"name\":\"International Journal of Emerging Trends in Engineering Research\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Emerging Trends in Engineering Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30534/ijeter/2023/031112023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Emerging Trends in Engineering Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30534/ijeter/2023/031112023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
Phishing Website Detection Using Ensemble Learning
Phishing is also the most common type of data breach. As a result, it is carried out by sending an email with links that lead to fraudulent websites. This technique is especially targeted to large companies. Usually, the attackers send emails with work-related information. Machine learning is one of the most successful techniques for detecting phishing. This paper analyzed the results of various machine learning techniques for predicting phishing websites. And also describes the various methods that are used to identify phishing websites. Some of these include the SVM classification method, Random Forest method, and AdaBoost method. Ensemble model that combines the SVM, Random Forest, and AdaBoost methods was able to classify a phishing site with an accuracy of 96%