Phishing Website Detection on URLs and Content Based Features Using Machine Learning

Phishing Website Detection on URLs and Content Based Features Using Machine Learning

Registration ID: IJNRD_225885

Published ID: IJNRD2407527

DOI: Click Here to Get

Gaganaspoorthy R , Dr. Manjunatha S

Phishing websites, Machine Learning, Content based features, URLs, Attacks

Phishing is a prevalent and perilous form of cybercrime. The objective of this assault is to illicitly obtain user information by gaining unauthorized access to the credentials utilized by individuals and organizations. Phishing websites typically offer a variety of clues and information that may be found on the web. Phishing sites attempt to obtain the victim's sensitive information by tricking them into visiting a website that seems similar to a legitimate one. This is a common type of criminal attack on the internet. Phishing websites are a type of cyber threat that aim to get sensitive information, such as credit card details and social security numbers. Currently, there is no definitive solution available to identify phishing assaults that is both reliable and unpredictable. This is due to the presence of multiple factors and criteria that are always changing. This research aims to utilize Machine Learning techniques to classify features, specifically Phishing Websites Data, in the UC Irvine Machine Learning Repository database. Web pages can be classified into various categories based on their properties. Therefore, in order to thwart phishing attempts, it is imperative to utilize a distinct characteristic of web pages. We have presented a model that utilizes machine learning techniques, specifically Naïve Bayes, to identify and classify phishing web pages. Various machine learning algorithms, including Naïve Bayes (NB), ANN, Random Forest, Adaboost, and XGBoost, were compared for results assessment. It was found that the identified algorithm achieved a high accuracy score.

Published Paper   E-Certificate

"Phishing Website Detection on URLs and Content Based Features Using Machine Learning", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 7, page no.g241-g255, July-2024, Available :https://ijnrd.org/papers/IJNRD2407527.pdf

Volume 9 Issue 7, July-2024

Pages : g241-g255

Paper Reg. ID: IJNRD_225885

Published Paper Id: IJNRD2407527

Downloads: 000121164

Research Area: Science & Technology

Country: Bangalore, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407527.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407527

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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