Authors :
A Naga Jyothi; Chimmili Mallika; Veliganti Jahnavi; Chintalapati Siva Naga; Adithya Varma; Kasani Chandra Shekar; Chitturi Sai Nirmal
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/mr2ffu4u
Scribd :
https://tinyurl.com/286x9te2
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1022
Abstract :
Website phishing has shown to be a serious
security risk. Phishing is the starting point for many
cyber attacks that compromise the confidentiality,
integrity, and availability of customer and business data.
Decades of effort have gone into developing innovative
techniques for automatically recognizing phishing
websites. Modern systems aren't very excellent at
spotting fresh phishing attacks and require a lot of
manual feature engineering, even though they can
produce better outcomes. Thus, an open problem in this
discipline is to identify tactics that can swiftly handle
zero-day phishing attempts and automatically recognize
phishing websites. The web page that is hosted at the
given URL has a lotof information that can be utilized
to assess the maliciousness of the web server. Machine
Learning is a useful technique to identify. Here, we
describe the characteristics of phishing domains, also
known as fraudulent domains, what sets them apart
from real domains, the significance of detecting these
domains, and how machine learning may be used to
detect them.
Keywords :
Cyber Attack, Phishing Websites, Machine Learning, Feature Engineering.
Website phishing has shown to be a serious
security risk. Phishing is the starting point for many
cyber attacks that compromise the confidentiality,
integrity, and availability of customer and business data.
Decades of effort have gone into developing innovative
techniques for automatically recognizing phishing
websites. Modern systems aren't very excellent at
spotting fresh phishing attacks and require a lot of
manual feature engineering, even though they can
produce better outcomes. Thus, an open problem in this
discipline is to identify tactics that can swiftly handle
zero-day phishing attempts and automatically recognize
phishing websites. The web page that is hosted at the
given URL has a lotof information that can be utilized
to assess the maliciousness of the web server. Machine
Learning is a useful technique to identify. Here, we
describe the characteristics of phishing domains, also
known as fraudulent domains, what sets them apart
from real domains, the significance of detecting these
domains, and how machine learning may be used to
detect them.
Keywords :
Cyber Attack, Phishing Websites, Machine Learning, Feature Engineering.
