Logo
  • Home
  • About Us
    • Aim and Scope
    • Research Area
    • Impact Factor
    • Indexing
  • For Authors
    • Authors Guidelines
    • How to publish paper?
    • Download Paper format
    • Submit Manuscript
    • Processing Charges
    • Download Copyrights Form
    • Submit Payment-Copyrights
  • Archives
    • Current Issues
    • Past Issues
    • Conference Issues
    • Special Issues
    • Advance Search
  • IJARIIE Board
    • Join as IJARIIE Board
    • Advisory Board
    • Editorial Board
    • Sr. Reviewer Board
    • Jr. Reviewer Board
  • Proposal
    • Conferece Proposal
    • Special Proposal
    • Faqs
  • Contact Us
  • Payment Detail

Call for Papers:Vol.11 Issue.4

Submission
Last date
28-Aug-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-4. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

  • How to Publish Paper
  • Submit Manuscript
  • Processing Charges
  • Submit Payment

Archives

  • Current Issue
  • Past Issue

IJARIIE Board

  • Member Of Board
  • Join As Board

Downloads

  • Authors Guidelines
  • Manuscript Template
  • Copyrights Form

Android App

Download IJARIIE APP
  • Authors
  • Abstract
  • Citations
  • Downloads
  • Similar-Paper

Authors

Title: :  PHISHING SITE DETECTION USING MACHINE LEARNING
PaperId: :  23092
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-23092
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
SOUNDARYA SBANNARIAMMAN INSTITUTE OF TECHNOLOGY
KAVIVARSINI SBANNARIAMMAN INSTITUTE OF TECHNOLOGY
DHARANISH ABANNARIAMMAN INSTITUTE OF TECHNOLOGY

Abstract

COMPUTER ENGINEERING
Phishing,Xgboost classifier, accuracy, machine learning, attacks,Url,Machine learning,algorithm,unbalanced data,handle missing values
Phishing sites represent a major concern in cybersecurity, posing significant risks to individuals and organizations alike. These deceptive websites mimic legitimate ones to trick users into divulging sensitive information such as login credentials, financial data, or personal details. The consequences of falling victim to phishing can range from identity theft and financial loss to compromising confidential information and breaching privacy. Combatting this threat requires a multi-faceted approach including user education, robust cybersecurity measures, and vigilance in detecting and reporting phishing attempts. Failure to address phishing sites effectively can result in widespread security breaches and undermine trust in online platforms and services.Numerous analysts have gone through many years making novel ways to deal with naturally distinguish phishing sites. While state of the art arrangements can convey better results, they need a ton of manual component designing and aren't great at recognizing new phishing assaults. Therefore, finding techniques that can naturally identify phishing sites and immediately oversee zero-day phishing endeavors is an open test in this field. The site page in the URL which has that contains an abundance of information that can be utilized to decide the web server's vindictiveness. AI is a powerful technique for recognizing phishing. It likewise disposes of the burdens of the past technique. The objective of this project is to train machine learning models on the dataset created to predict phishing websites. Both phishing and benign urls of websites are gathered to form a dataset and from them required url and website content-based features are extracted. The performance level of model is calculated. The workflow involves preprocessing the dataset to handle missing values and standardizing features. The Xgboost model is then trained on the labeled dataset, utilizing a gradient boosting framework to build an ensemble of decision trees. Furthermore, the system is integrated into a real-time monitoring tool that continuously assesses web pages for potential phishing threats. The tool provides timely alerts to users, enabling them to make informed decisions about the legitimacy 2 of websites they interact with. Based on the accuracy, the ability to handle missing value and speed we have used Xgboost classifier to predict the phishing website. The Xgboost based phishing site detection system demonstrates its capability to adapt to evolving threats in the dynamic landscape of online security, thereby mitigating the risks associated with phishing attacks.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE SOUNDARYA S, KAVIVARSINI S, and DHARANISH A. "PHISHING SITE DETECTION USING MACHINE LEARNING" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 2668-2673
MLA SOUNDARYA S, KAVIVARSINI S, and DHARANISH A. "PHISHING SITE DETECTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 2668-2673.
APA SOUNDARYA S, KAVIVARSINI S, & DHARANISH A. (2024). PHISHING SITE DETECTION USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 2668-2673.
Chicago SOUNDARYA S, KAVIVARSINI S, and DHARANISH A. "PHISHING SITE DETECTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 2668-2673.
Oxford SOUNDARYA S, KAVIVARSINI S, and DHARANISH A. 'PHISHING SITE DETECTION USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 2668-2673. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/PHISHING_SITE_DETECTION_USING_MACHINE_LEARNING_ijariie23092.pdf (Accessed : 05 April 2024).
Harvard SOUNDARYA S, KAVIVARSINI S, and DHARANISH A. (2024) 'PHISHING SITE DETECTION USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 2668-2673IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/PHISHING_SITE_DETECTION_USING_MACHINE_LEARNING_ijariie23092.pdf (Accessed : 05 April 2024)
IEEE SOUNDARYA S, KAVIVARSINI S, and DHARANISH A, "PHISHING SITE DETECTION USING MACHINE LEARNING," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 2668-2673, Mar-App 2024. [Online]. Available: http://ijariie.com/AdminUploadPdf/PHISHING_SITE_DETECTION_USING_MACHINE_LEARNING_ijariie23092.pdf [Accessed : 05 April 2024].
Turabian SOUNDARYA S, KAVIVARSINI S, and DHARANISH A. "PHISHING SITE DETECTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 (05 April 2024).
Vancouver SOUNDARYA S, KAVIVARSINI S, and DHARANISH A. PHISHING SITE DETECTION USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 05 April 2024]; 10(2) : 2668-2673. Available from: http://ijariie.com/AdminUploadPdf/PHISHING_SITE_DETECTION_USING_MACHINE_LEARNING_ijariie23092.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads


Last download on 4/5/2024 9:45:53 AM

Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Breath Based Bio- Signal Analyser for Disease DetectionArtificial Intelligence and ElectricalsDharshini K Download
TEACHING WITH AI: LIVED EXPERIENCES, RESPONSIBILITIES, AND CHALLENGES OF TECH-TECHERSEducationJaneth M. Wariza Download
USER’S PERSPECTIVES ON AI- POWERED VIRTUAL ASSISTANT IN EDUCATION AND EVERYDAY LIFEEducational TechnologyRechelle M. Cardines Download
FLAP-AI BIRDArtificial Intelligence and Machine LearningRamya BN Download
AL-Powered healthcare chatbot for personalized medical assistance and wound classificationAI-Powered Healthcare SystemProf. Manjunath N Download
AI-Powered Resume Generation: A Streamlit-Based System Using PyTorch for Intelligent Career ProfilingComputer EngineeringAditi Meshram Download
AI based object color detection and sorting automationRobotics and AutomationBHUVAN THEJASWI T Download
Advanced knowledge on Synthetic Data Generation Using GANs for Medical Imaging ApplicationsArtificial IntelligenceRajeev Ranjan Download
Information on Cognitive AI Assistants for Visually Impaired Navigation SupportComputer ScienceNagaraj.H Download
Speech Emotion AI for Mental Health Monitoring in Call CentersComputer ScienceGowtham Kumar Download
Multi-Agent Reinforcement Learning for Coordinated Drone SwarmsComputer ScienceDeepak Gowda Download
AI-Based Soil Health Monitoring Using Remote Sensing and Deep LearningArtificial IntelligenceChethan Nagraj Download
Dynamic Criminal Network Link Forecasting via Deep Reinforcement LearningDeep LearningSrikant Tayade Download
Virtual Try-On-Enhancing Fashion Exploration for Gen-ZDeep Learning & Generative ModelsAnushka Mane Download
To Develop an EMG-Based Expert System for the Detection of Wrist Flexion and ExtensionArtificial Intelligence and Data ScienceRohit Mehta Download
12
For Authors
  • Submit Paper
  • Processing Charges
  • Submit Payment
Archive
  • Current Issue
  • Past Issue
IJARIIE Board
  • Member Of Board
  • Join As Board
Privacy and Policy
Follow us

Contact Info
  • +91-8401209201 (India)
  • +86-15636082010 (China)
  • ijariiejournal@gmail.com
  • M-20/234 Ami Appt,
    Nr.Naranpura Tele-Exch,
    Naranpura,
    Ahemdabad-380063
    Gujarat,India.
Copyright © 2025. IJARIIE. All Rights Reserved.