SaleSurf - Strengthening the Web's Armour, One URL at a Time - Malicious URL Detection using Machine Learning Models

Sunidhi Rathod; Nehal Panchal; Atul Sarowa; Prof. Vricha Chavhan

doi:10.36948/ijfmr.2024.v06i02.18240

SaleSurf - Strengthening the Web's Armour, One URL at a Time - Malicious URL Detection using Machine Learning Models

Author(s)	Sunidhi Rathod, Nehal Panchal, Atul Sarowa, Prof. Vricha Chavhan
Country	India
Abstract	One of the most common cybersecurity vulnerabilities involves malicious websites or URLs. Every year, individuals and organizations suffer major financial losses from using harmful content such as spam, malware, inappropriate advertising, and scams that encourage visitors to cheat. These malicious URLs are often promoted through emails, advertisements, web search results, or links to other websites. Considering how many users click on these malicious URLs, there is an urgent need for a reliable system that can classify and identify dangerous URLs; In particular, phishing, spam and malware attacks are increasing. Data volume, updated attack models and strategies, correlation between URL features, lack of data, inconsistent data and the presence of outliers make the division of labor very difficult. In our research, we focus on negative URL search to gain more insight. Our information is divided into four main categories: phishing, harmless (safe), tampering, and malware. We have collected a large database of 651,191 URLs to support our application. To achieve the goal of identifying and identifying malicious URLs, we use three machine learning algorithms: Random Forest, LightGBM, XGBoost, Logistic Regression, CNN and Ensemble Model.
Keywords	URL discovery, network security, machine learning, URL isolation, phishing, benign, tampering, malware.
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-04-25
Cite This	SaleSurf - Strengthening the Web's Armour, One URL at a Time - Malicious URL Detection using Machine Learning Models - Sunidhi Rathod, Nehal Panchal, Atul Sarowa, Prof. Vricha Chavhan - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18240
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.18240
Short DOI	https://doi.org/gtsg6w

View / Download PDF File

E-ISSN 2582-2160

doi

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 6 Cover Page Vol 6 Isu 5 Cover 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

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

International Journal For Multidisciplinary Research

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

SaleSurf - Strengthening the Web's Armour, One URL at a Time - Malicious URL Detection using Machine Learning Models

Share this