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Rathore, Sandhya (2021) Detection of Web-based Phishing URL using Machine Learning, Whitelist and Blacklist approach. Masters thesis, Dublin, National College of Ireland.
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Abstract
Phishing cases have only been rising with each passing day, 36% of the data breaches are Phishing-based as per the report submitted by Verizon for the Year 2021. Individuals are deceived using various methods including emails, instant messages and advertisements on the website to click or browse to a malicious website. A lot of money transactions, around 13 percent of total E-commerce sales are occurring on daily basis involving Internet banking and Online-shopping, users are being directed to malicious websites which appear similar to the identical websites. Deceptive phishing can be done in various ways for obtaining the credentials of a user. Anti-phishing tools have been developed for detecting phishing attacks still the rate of phishing cases is rising every year.
Due to the increasing number of phishing websites which is 640% in the year 2020 revealed by Webroot, there is a chance of Cyber-attacks taking place. To prevent the cyberattacks taking place, experts have suggested to improve the existing anti-phishing methods. It is suggested to use multiple anti-phishing techniques at once to detect the phishing attacks. Recently, due to the covid break individuals and companies are facing a greater number of phishing attacks, they are being directed to fake websites and asked to enter their credentials. Cybercriminals are finding a greater number of opportunities to do the phishing attempts. Kernel-methods are popular which includes the Support Vector Machine algorithm (SVM). SVM can classify the given data into linear and non-linear. The solution suggested for this project is the combination listing-based methods and SVM to detect malicious URLs.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Tamara Malone |
| Date Deposited: | 29 Dec 2022 14:41 |
| Last Modified: | 07 Mar 2023 12:28 |
| URI: | https://norma.ncirl.ie/id/eprint/6048 |
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