International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 6
November-December 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Detection Of phishing URLs using a Term Frequency Inverse Document Frequency (TF-IDF).

Author(s) Sibhathallah M, Sathya Srinivas D
Country India
Abstract Phishing attacks continue to pose a threat to online security as hackers employ more sophisticated tactics to trick users into revealing sensitive information. One common method is creating fake login URLs that mimic legitimate websites, making it challenging for users to distinguish between safe and dangerous links. This study focuses on detecting real-world phishing URLs, particularly analyzing login URLs. Our aim is to develop effective techniques and tools for early detection and prevention of phishing attempts by examining the common traits and patterns associated with them. The paper seeks to enhance people's and organizations' resilience to phishing attacks by exploring advanced technology and machine learning algorithms, ultimately contributing to a safer online environment. The research evaluates the effectiveness of deep learning and machine learning techniques in classifying phishing URLs, using statistical features such as Term Frequency-Inverse Document Frequency (TF-IDF) in combination with character N-gram, as well as proposed handcrafted features. CNN models are utilized for the deep learning approaches. Following model training, it is used to classify phishing URLs. The primary aim of this experiment is to assess the effectiveness of our proposed method. The results demonstrate that our phishing URL detection method achieves an accuracy rate of 96.6%
Keywords Cybercrime, Machine Learning, URLs, Prediction, Phishing Detection.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-06-01
Cite This Detection Of phishing URLs using a Term Frequency Inverse Document Frequency (TF-IDF). - Sibhathallah M, Sathya Srinivas D - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.21435
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.21435
Short DOI https://doi.org/gtw6rt
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com