Authors :
Kotturu Riteesh; Yarramaneni Maruthi Chowdary; Gudiseva Naga Sai Teja; Dr. A. Srisaila
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3RUPTMf
DOI :
https://doi.org/10.5281/zenodo.6879293
Abstract :
— In the last few years, the web phishing attacks
have been constantly evolving, causing the customers to
lose their trust in e-commerce, online services, trading
platforms and new contacts. Various types of tools and
systems based on a blacklist of phishing websites are
applied to detect the phishing websites. Unfortunately,
the daily continuous evolution of technology has led to the
birth of more sophisticated methods when building
websites to attract potential users. There is an increase in
recent research studies they have been adopting machine
learning techniques to identify phishing websites and
utilizing them for early alarm methods to identify such
threats. Phishing website detection is proposed using
particle swarm optimization-based feature weighting is
proposed to enhance the detection of phishing websites.
The proposed approach of our work suggests the
utilization of particle swarm optimization (PSO) to
analyze various records of website features effectively to
achieve higher accuracy while detecting phishing
websites. The experimental results indicated that the
proposed PSO-based feature analysis which achieved an
outstanding improvement in the terms of classifying the
accuracy and determining the best algorithm approach
Keywords :
Random Forest, Decision Tree, Internet Identity, SVM, Machine learning, Logistic regression, Multilayer perceptron.
— In the last few years, the web phishing attacks
have been constantly evolving, causing the customers to
lose their trust in e-commerce, online services, trading
platforms and new contacts. Various types of tools and
systems based on a blacklist of phishing websites are
applied to detect the phishing websites. Unfortunately,
the daily continuous evolution of technology has led to the
birth of more sophisticated methods when building
websites to attract potential users. There is an increase in
recent research studies they have been adopting machine
learning techniques to identify phishing websites and
utilizing them for early alarm methods to identify such
threats. Phishing website detection is proposed using
particle swarm optimization-based feature weighting is
proposed to enhance the detection of phishing websites.
The proposed approach of our work suggests the
utilization of particle swarm optimization (PSO) to
analyze various records of website features effectively to
achieve higher accuracy while detecting phishing
websites. The experimental results indicated that the
proposed PSO-based feature analysis which achieved an
outstanding improvement in the terms of classifying the
accuracy and determining the best algorithm approach
Keywords :
Random Forest, Decision Tree, Internet Identity, SVM, Machine learning, Logistic regression, Multilayer perceptron.
