利用机器学习检测恶意 URL

International Journal of Integrated Science and Technology Pub Date : 2024-01-31 DOI:10.59890/ijist.v2i1.1289

Prenalee Nanaware

{"title":"利用机器学习检测恶意 URL","authors":"Prenalee Nanaware","doi":"10.59890/ijist.v2i1.1289","DOIUrl":null,"url":null,"abstract":"\n\n\n\n One of the most prevalent and least protected security risks in existence today is fraudulent websites and URLs.We offer a method that both uses machine learning characteristics to identify phishing URLs and employs text processing techniques to evaluate text and identify incorrect remarks that are suggestive of phishing assaults.\n\n\n\n","PeriodicalId":503863,"journal":{"name":"International Journal of Integrated Science and Technology","volume":"57 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Malicious URL Detection using Machine Learning\",\"authors\":\"Prenalee Nanaware\",\"doi\":\"10.59890/ijist.v2i1.1289\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\n\\n\\n One of the most prevalent and least protected security risks in existence today is fraudulent websites and URLs.We offer a method that both uses machine learning characteristics to identify phishing URLs and employs text processing techniques to evaluate text and identify incorrect remarks that are suggestive of phishing assaults.\\n\\n\\n\\n\",\"PeriodicalId\":503863,\"journal\":{\"name\":\"International Journal of Integrated Science and Technology\",\"volume\":\"57 \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Integrated Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.59890/ijist.v2i1.1289\",\"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 Integrated Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59890/ijist.v2i1.1289","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

我们提供的方法既能利用机器学习特征识别网络钓鱼 URL，又能利用文本处理技术评估文本并识别暗示网络钓鱼攻击的错误备注。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Malicious URL Detection using Machine Learning

One of the most prevalent and least protected security risks in existence today is fraudulent websites and URLs.We offer a method that both uses machine learning characteristics to identify phishing URLs and employs text processing techniques to evaluate text and identify incorrect remarks that are suggestive of phishing assaults.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Journal of Integrated Science and Technology

自引率

0.00%

发文量