Keerthika J, Adisvara A, Akash S, Jayanesh B, Arul Prakash T
{"title":"E-mail Spam Detection and Phishing link Detection Using Machine Learning","authors":"Keerthika J, Adisvara A, Akash S, Jayanesh B, Arul Prakash T","doi":"10.53759/acims/978-9914-9946-9-8_9","DOIUrl":null,"url":null,"abstract":"Phishing, which tricks individuals into revealing delicatedata like login credentials and financial details, is the most widespread type of cybercrime. Attackers typically use electronic mail, prompt messaging, and telephone calls to initiate these attacks. Despite ongoing efforts to prevent phishing attacks, current measures are not entirely effective, as the amount of phishing emails has enlarged significantly in current years. While numerous methods have been developed to filter out phishing emails, there is still a need for a comprehensive solution. This survey is the first of its kind to examine the use of N-L-P and ML methods for identifying phishing electronic mail. The analyzesof state_of_the_art N- L-P approaches that are presently being used to detect phishing electronic mail at different periods of the outbreak, with a focus on M-L methods. These methods are compared and evaluated in-depth.","PeriodicalId":261928,"journal":{"name":"Advances in Computational Intelligence in Materials Science","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Computational Intelligence in Materials Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53759/acims/978-9914-9946-9-8_9","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
Phishing, which tricks individuals into revealing delicatedata like login credentials and financial details, is the most widespread type of cybercrime. Attackers typically use electronic mail, prompt messaging, and telephone calls to initiate these attacks. Despite ongoing efforts to prevent phishing attacks, current measures are not entirely effective, as the amount of phishing emails has enlarged significantly in current years. While numerous methods have been developed to filter out phishing emails, there is still a need for a comprehensive solution. This survey is the first of its kind to examine the use of N-L-P and ML methods for identifying phishing electronic mail. The analyzesof state_of_the_art N- L-P approaches that are presently being used to detect phishing electronic mail at different periods of the outbreak, with a focus on M-L methods. These methods are compared and evaluated in-depth.