{"title":"Phishing Email’s Detection Using Machine Learning and Deep Learning","authors":"Nishant Santosh Paradkar","doi":"10.1109/ACCESS57397.2023.10200493","DOIUrl":null,"url":null,"abstract":"In today’s world, all of us are dependent on emails. Emails are a very efficient and fast way of sending a message to someone. But malicious users often use it to send fraudulent emails with fake links that steal user credentials like credit card details, login-id, passwords, etc. These emails are called phishing emails. These emails constitute identity fraud as the emails are interpreted to be from banks or other multinational companies. Many existing solutions require the user to check for grammar errors, check the email-id, or avoid clicking any links. But all these actions require human involvement. In this paper, I have implemented and compared current Machine Learning and Deep Learning techniques used with Natural Language Processing to detect phishing emails and achieved an accuracy of 98%.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACCESS57397.2023.10200493","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In today’s world, all of us are dependent on emails. Emails are a very efficient and fast way of sending a message to someone. But malicious users often use it to send fraudulent emails with fake links that steal user credentials like credit card details, login-id, passwords, etc. These emails are called phishing emails. These emails constitute identity fraud as the emails are interpreted to be from banks or other multinational companies. Many existing solutions require the user to check for grammar errors, check the email-id, or avoid clicking any links. But all these actions require human involvement. In this paper, I have implemented and compared current Machine Learning and Deep Learning techniques used with Natural Language Processing to detect phishing emails and achieved an accuracy of 98%.