PHISHGUARD PRO: PHISHING DETECTION AND PREVENTION

Registration ID: IJNRD_218486

Published ID: IJNRD2405180

DOI: Click Here to Get

Rushikesh Ghonmode , Aditya Shimpi , Priyam Shrivastav , Dhanshree Wadnere

This study proposes an intelligent model for detecting phishing websites using Extreme Learning Machine (ELM). Phishing websites attempt to extract confidential data by mimicking legitimate sites. Our approach involves preprocessing a dataset of phishing and legitimate URLs, extracting features such as domain, address, abnormal attributes, HTML, and JavaScript features. Machine learning techniques, specifically Random Forest and Support Vector Machine (SVM), are employed for classification based on URL attributes. The system computes range and threshold values for classification, aiming to detect phishing instances effectively. By leveraging feature extraction and realtime detection, our model contributes to mitigating the risks associated with phishing attacks, enhancing user and organizational security

Published Paper   E-Certificate

Rushikesh Ghonmode, Aditya Shimpi, Priyam Shrivastav, & Dhanshree Wadnere (May-2024). PHISHGUARD PRO: PHISHING DETECTION AND PREVENTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), b648-b649. https://ijnrd.org/papers/IJNRD2405180.pdf

Volume 9 Issue 5, May-2024

Pages : b648-b649

Paper Reg. ID: IJNRD_218486

Published Paper Id: IJNRD2405180

Downloads: 000121988

Research Area: Computer Science & Technology 

Country: Nashik, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405180.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405180

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

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

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