PDMLP: Phishing Detection using Multilayer Perceptron

Saad Al-Ahmadi, Tariq Lasloum
{"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}
引用次数: 14

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.
PDMLP:基于多层感知器的网络钓鱼检测
网络钓鱼网站是互联网上的一个重大问题。这是一种网络攻击类型,攻击者试图获取敏感信息,如用户名、密码或信用卡信息。最近在许多网站上部署检测网络钓鱼URL系统的增长导致了大量可用数据来预测网络钓鱼网站。本文提出了一种基于多层感知器(PDMLP)的网络钓鱼检测系统,该系统用于两种类型的数据集。这些机制的性能评估方面的准确性,精密度,召回率和f测量。结果表明,与KNN、SVM、C4.5决策树、RF和RoF等分类器相比,PDMLP具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信