PhisherDock

Registration ID: IJNRD_195034

Published ID: IJNRD2305333

: http://doi.one/10.1729/Journal.34206

Dr. Dhananjaya V, Anirudh Rai

Phishing Detection, Chrome Extension, Machine Learning

Phishing is a fraudulent technique used to extract sensitive data and user credentials by impersonating legitimate websites. Cybercriminals often create duplicate websites with malicious code to steal personal information from unsuspecting users. Such attacks can cause significant financial damage to individuals and businesses using banking and financial services. Traditionally, blacklists of known phishing links or heuristic analysis of suspicious web pages have been used to detect phishing attacks. However, heuristic functions rely on trial and error, resulting in poor accuracy and low adaptability to new phishing links. The primary objective of the project is to develop a machine learning-based solution to identify and block phishing and malicious web links. The aim is to build an advanced software product that employs machine learning algorithms to recognize and flag potentially harmful URLs. This approach will involve leveraging machine learning to overcome these limitations by implementing various classification algorithms and evaluating their performance on our dataset.

Published Paper   E-Certificate

Dr. Dhananjaya V & Anirudh Rai (May-2023). PhisherDock. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), d234-d240. http://doi.one/10.1729/Journal.34206

Volume 8 Issue 5, May-2023

Pages : d234-d240

Paper Reg. ID: IJNRD_195034

Published Paper Id: IJNRD2305333

Downloads: 000122254

Research Area: Engineering

Author Type: Indian Author

Country: Bangalore, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305333.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305333

Crossref DOI: http://doi.one/10.1729/Journal.34206

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