Detection of Phishing Websites Using PSO and Machine Learning Frameworks

Authors : Kotturu Riteesh; Yarramaneni Maruthi Chowdary; Gudiseva Naga Sai Teja; Dr. A. Srisaila

Volume/Issue : Volume 7 - 2022, Issue 6 - June

Google Scholar :

Scribd :


— 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.


Paper Submission Last Date
31 - March - 2024

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.