|
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter
Copyright Infringement Claims
Indexing Partner
|
Published Paper Details
|
|
| Paper Title: | A Comprehensive Study on Enhancing Phishing Website Detection through Advanced Machine Learning |
| Authors Name: | Sourav Prakash Gutte , Prof. Tanvi Ghodake , Vaibhav Jagtap , Vishal Kudale |
| Unique Id: | IJSDR2311098 |
| Published In: | Volume 8 Issue 11, November-2023 |
| Abstract: | Phishing, the fraudulent practice of deceiving users into revealing personal or financial information by posing as a legitimate entity, has become an increasingly prevalent and sophisticated cyber threat. Traditional phishing detection methods, which rely on rule-based or blacklist approaches, are often ineffective against these evolving attacks. Advanced machine learning (ML) techniques, with their ability to learn from data and identify patterns, offer promising solutions for enhancing phishing website detection. This study explores the current state of ML-based phishing website detection and proposes a novel approach that utilizes deep learning algorithms to extract and analyze complex features from phishing websites. The proposed method employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to effectively capture both visual and contextual information from websites. Experimental results demonstrate that the proposed approach achieves significantly higher accuracy in phishing website detection compared to traditional methods and existing ML-based techniques. |
| Keywords: | Feature Extraction, SVM Classification, Model-training. |
| Cite Article: | "A Comprehensive Study on Enhancing Phishing Website Detection through Advanced Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 11, page no.652 - 655, November-2023, Available :http://www.ijsdr.org/papers/IJSDR2311098.pdf |
| Downloads: | 000338719 |
| Publication Details: | Published Paper ID: IJSDR2311098 Registration ID:209344 Published In: Volume 8 Issue 11, November-2023 DOI (Digital Object Identifier): Page No: 652 - 655 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijsdr.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
Track Paper
Important Links
Conference Proposal
ISSN
 |
 |
DOI (A digital object identifier)
  Providing A digital object identifier by DOI How to GET DOI and Hard Copy Related |
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
 |
 |
 |
Social Media
Indexing Partner |
|||
| Copyright © 2024 - All Rights Reserved - IJSDR | |||
Facebook Twitter Instagram LinkedIn