Authors :
T.Pavansai; Ziaul Haque Choudhury; G.Gowtham sai
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/5ctchdhs
DOI :
https://doi.org/10.5281/zenodo.8199583
Abstract :
The problem with cyber security involves scam
websites, stilling the information that exploit people's
trust. It could be reduced to the act of enticing internet
users even though that they can get their personal data,
including user names and passwords. In this study, we
present a method for identifying phishing websites. The
technology works as an add-on to a web browser, alerting
the user when it finds a phishing website. A machine
learning technique, specifically supervised learning is
proposed in our study. The Logistic regression, Principal
Component Analysis (PCA) and Apriori algorithms are
chosen because of its success in classification. By
examining the characteristics of phishing websites and
selecting strongest combination of them, we developed a
classifier that performs better.
Keywords :
Phishing Website, Cyber Security, Machine Learning.
The problem with cyber security involves scam
websites, stilling the information that exploit people's
trust. It could be reduced to the act of enticing internet
users even though that they can get their personal data,
including user names and passwords. In this study, we
present a method for identifying phishing websites. The
technology works as an add-on to a web browser, alerting
the user when it finds a phishing website. A machine
learning technique, specifically supervised learning is
proposed in our study. The Logistic regression, Principal
Component Analysis (PCA) and Apriori algorithms are
chosen because of its success in classification. By
examining the characteristics of phishing websites and
selecting strongest combination of them, we developed a
classifier that performs better.
Keywords :
Phishing Website, Cyber Security, Machine Learning.
