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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

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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
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