IJNRD Research Journal

WhatsApp
Click Here

WhatsApp editor@ijnrd.org
IJNRD
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.76

Issue per Year : 12

Volume Published : 9

Issue Published : 96

Article Submitted :

Article Published :

Total Authors :

Total Reviewer :

Total Countries :

Indexing Partner

Join RMS/Earn 300

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: PhisherDock
Authors Name: Dr. Dhananjaya V , Anirudh Rai
Download E-Certificate: Download
Author Reg. ID:
IJNRD_195034
Published Paper Id: IJNRD2305333
Published In: Volume 8 Issue 5, May-2023
DOI: http://doi.one/10.1729/Journal.34206
Abstract: 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.
Keywords: Phishing Detection, Chrome Extension, Machine Learning
Cite Article: "PhisherDock", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.d234-d240, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305333.pdf
Downloads: 000118754
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
Publication Details: Published Paper ID:IJNRD2305333
Registration ID: 195034
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.34206
Page No: d234-d240
Country: Bangalore, Karnataka, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305333
Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305333
Share Article:
Share

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from www.ijnrd.org
Semantic Scholar Microsaoft Academic ORCID Zenodo
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX PUBLON
DRJI SSRN Scribd DocStoc

ISSN Details

ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to Get DOI? DOI

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Social Media

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Join RMS/Earn 300

IJNRD