Paper Title
Phishing Website Detection Using Machine Learning
Article Identifiers
: Keywords
Phishing, Phishing website detection, Machine Learning, KNN, SVM, Random Forest
Abstract
The Internet's advancement has drawn attention to network security, as a secure network environment is fundamental for the Internet's fast and healthy growth. Cybercriminals employ phishing, a malicious act of deceiving users into clicking on phishing links, stealing their information, and using it to fake logins and steal funds. Network security is an iterative issue of attack and defense, and phishing and its detection technology continually evolve. Blacklists and whitelists are traditional methods for identifying phishing links but fail to identify new ones, which necessitates predicting whether a new link is a phishing website and improving the prediction's accuracy. Machine learning has emerged as a critical tool in predicting phishing websites, with this paper offering system learning technology for the detection of phishing URLs via extracting and studying diverse functions of valid and phishing URLs. KNN Classifier, Random Forest, and Support Vector Machine algorithms are used to locate phishing websites. The paper aims to detect phishing URLs as well as narrow them down to the fine algorithm that gets to know the set of rules with the aid of using evaluating the accuracy rate.
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How To Cite (APA)
Fouziya Farheen, Balineni Ram Deepak, Nirupam Kumar Sasapu, & Sagar Chanchlani (March-2023). Phishing Website Detection Using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), e13-e20. https://ijnrd.org/papers/IJNRD2303403.pdf
Issue
Volume 8 Issue 3, March-2023
Pages : e13-e20
Other Publication Details
Paper Reg. ID: IJNRD_189732
Published Paper Id: IJNRD2303403
Downloads: 000121993
Research Area: Engineering
Author Type: Indian Author
Country: Visakhapatnam, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2303403.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303403
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