MACHINE LEARNING APPROACH FOR DETECTION OF PHISHING WEBSITE
DESIGN DETAILS
While cybersecurity attacks continue to escalate in both scale and sophistication, social engineering approaches are still some of the simplest and most effective ways to gain access to sensitive or confidential information. The United States Computer Emergency Readiness Team (US-CERT) defines phishing as a form of social engineering that uses e-mails or malicious websites to solicit personal information from an individual or company by posing as a trustworthy organization or entity. While organizations should educate employees about how to recognize phishing e-mails or links to help protect against the above types of attacks, software such as HTTrack is readily available for users to duplicate entire websites for their own purposes. As a result, even trained users can still be tricked into revealing private or sensitive information by interacting with a malicious website that they believe to be legitimate. This Matlab design is to develop methods of utilizing various approaches to categorize websites specifically uses machine learning techniques to classify websites based on their URL. Four classifiers used in the design,
1. Decision Tree,
2. Naive Bayesian classifier,
3. Support Vector Machine (SVM), and
4. Neural Network.
DOWNLOAD THE MATLAB(P-CODE) FROM THE BELOW URL,
https://drive.google.com/file/d/10sek...
You may contact +91 7904568456 by WhatsApp Chat, for paid services.
We are available on Telegram and Signal.
Visit Website: http://www.verilogcourseteam.com/
Visit Our Social Media
Like our Facebook Page: / verilogcourseteam
Subscribe: / verilogcourseteamelectricalprojects
Subscribe: / verilogcourseteammatlabproject
Subscribe: / verilogcourseteam