Abstract
Phishing is a type of cyberattack in which a phoney website imitates a genuine, authentic website. Because the website gives the impression that it is legitimate, users divulge sensitive information online, including credit card numbers, passwords, PINs, and social security numbers. These websites pose a serious risk to internet users since they contain extremely sensitive information, making their detection and blocking essential. In order to shield internet users from these kinds of attacks, we present a novel phishing detection technique in this thesis. Specifically, our suggested approach may distinguish between a legal website and a phishing website based only on a screenshot of the website's logo. Any hidden logo won't be able to fool the algorithm into thinking the website is phishing, as was the case with previous solutions, because the screenshot is used to extract the logo. The first study concentrated on collecting datasets before extracting the logo image. The URLs linked to this image are returned by an automated script that uploads the logo image to the Google image search engine. It makes sense to treat the logo image as the original URL's identity because the logo and domain name have an exclusive link. As a result, when Google searches for the logo picture, the phishing website will not be retrieved as a URL and will not have the same relationship to the logo image as such. Additionally, the detection accuracy is strengthened by using Alexa page rank.
Keywords
Anti-phishing; Website logo; Google image search, Image Processing, etc.
Citations
IRE Journals:
K. Mani Raju, Gattu Ramya, Nagavelli Yogender Nath, R. Prasanth Reddy "Logo Image Focused Method for Phishing Detection" Iconic Research And Engineering Journals Volume 7 Issue 11 2024 Page 862-869 https://doi.org/10.64388/IREV7I11-1712451
IEEE:
K. Mani Raju, Gattu Ramya, Nagavelli Yogender Nath, R. Prasanth Reddy
"Logo Image Focused Method for Phishing Detection" Iconic Research And Engineering Journals, 7(11) https://doi.org/10.64388/IREV7I11-1712451
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