{"title":"PDMLP:基于多层感知器的网络钓鱼检测","authors":"Saad Al-Ahmadi, Tariq Lasloum","doi":"10.5121/ijnsa.2020.12304","DOIUrl":null,"url":null,"abstract":"A phishing website is a significant problem on the internet. It’s one of the Cyber-attack types where attackers try to obtain sensitive information such as username and password or credit card information. The recent growth in deploying a Detection phishing URL system on many websites has resulted in a massive amount of available data to predict phishing websites. In this paper, we purpose a new method to develop a phishing detection system called phishing detection based on a multilayer perceptron (PDMLP), which used on two types of datasets. The performance of these mechanisms evaluated in terms of Accuracy, Precision, Recall, and F-measure. Results showed that PDMLP provides better performance in comparison to KNN, SVM, C4.5 Decision Tree, RF, and RoF to classifiers.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"9 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"PDMLP: Phishing Detection using Multilayer Perceptron\",\"authors\":\"Saad Al-Ahmadi, Tariq Lasloum\",\"doi\":\"10.5121/ijnsa.2020.12304\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A phishing website is a significant problem on the internet. It’s one of the Cyber-attack types where attackers try to obtain sensitive information such as username and password or credit card information. The recent growth in deploying a Detection phishing URL system on many websites has resulted in a massive amount of available data to predict phishing websites. In this paper, we purpose a new method to develop a phishing detection system called phishing detection based on a multilayer perceptron (PDMLP), which used on two types of datasets. The performance of these mechanisms evaluated in terms of Accuracy, Precision, Recall, and F-measure. Results showed that PDMLP provides better performance in comparison to KNN, SVM, C4.5 Decision Tree, RF, and RoF to classifiers.\",\"PeriodicalId\":93303,\"journal\":{\"name\":\"International journal of network security & its applications\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of network security & its applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/ijnsa.2020.12304\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of network security & its applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/ijnsa.2020.12304","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PDMLP: Phishing Detection using Multilayer Perceptron
A phishing website is a significant problem on the internet. It’s one of the Cyber-attack types where attackers try to obtain sensitive information such as username and password or credit card information. The recent growth in deploying a Detection phishing URL system on many websites has resulted in a massive amount of available data to predict phishing websites. In this paper, we purpose a new method to develop a phishing detection system called phishing detection based on a multilayer perceptron (PDMLP), which used on two types of datasets. The performance of these mechanisms evaluated in terms of Accuracy, Precision, Recall, and F-measure. Results showed that PDMLP provides better performance in comparison to KNN, SVM, C4.5 Decision Tree, RF, and RoF to classifiers.