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Title: :  PHISHING WEBSITE DETECTION USING MACHINE LEARNING
PaperId: :  20360
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 3 2023
DUI:    16.0415/IJARIIE-20360
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Pooja PBangalore Institute of Technology
Harshith Reddy GBangalore Institute of Technology
Hrithik KBangalore Institute of Technology
Devanapalli NikhilBangalore Institute of Technology
Bala Sai Yaswanth YadavBangalore Institute of Technology

Abstract

Computer Engineering
Phishing Detection , URLs, Machine Learning, Support Vector Machine, Random Forest, Linear Regression
With the increasing integration of the Internet into our social and professional lives, we are exposed to a growing number of serious security attacks. One of the primary concerns that needs immediate attention is the ability to detect various network threats, particularly those that involve attacks not seen before. Phishing, in particular, poses a significant risk as attackers aim to collect private information such as user identities, passwords, and financial transactions by creating websites that closely resemble legitimate ones. Government and financial organizations, being popular online destinations for users, have experienced a significant increase in phishing threats and attacks over the years.To combat these evolving phishing methods, it is crucial to employ anti-phishing techniques that can effectively detect and prevent such attacks. Machine learning presents a powerful tool for countering phishing assaults. Various machine learning approaches have been proposed and implemented to identify phishing websites. In this project, three supervised classification models are utilized: Support Vector Machine, Random Forest Classifier, and Logistic Regression.Among these models, the Random Forest Classifier has been found to provide the highest accuracy for the selected dataset, achieving an accuracy score of 97%. This classifier leverages the power of ensemble learning by combining multiple decision trees to make predictions. By using a combination of features extracted from URLs and employing the trained model, it becomes possible to accurately classify websites as phishing or non-phishing.By implementing these machine learning techniques, the project aims to enhance the detection and prevention of phishing attacks, thereby mitigating the risks associated with online security threats.

Citations

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IJARIIE Pooja P, Harshith Reddy G, Hrithik K, Devanapalli Nikhil, and Bala Sai Yaswanth Yadav. "PHISHING WEBSITE DETECTION USING MACHINE LEARNING" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 3 2023 Page 1930-1935
MLA Pooja P, Harshith Reddy G, Hrithik K, Devanapalli Nikhil, and Bala Sai Yaswanth Yadav. "PHISHING WEBSITE DETECTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 9.3(2023) : 1930-1935.
APA Pooja P, Harshith Reddy G, Hrithik K, Devanapalli Nikhil, & Bala Sai Yaswanth Yadav. (2023). PHISHING WEBSITE DETECTION USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education, 9(3), 1930-1935.
Chicago Pooja P, Harshith Reddy G, Hrithik K, Devanapalli Nikhil, and Bala Sai Yaswanth Yadav. "PHISHING WEBSITE DETECTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 3 (2023) : 1930-1935.
Oxford Pooja P, Harshith Reddy G, Hrithik K, Devanapalli Nikhil, and Bala Sai Yaswanth Yadav. 'PHISHING WEBSITE DETECTION USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, 2023, p. 1930-1935. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/PHISHING_WEBSITE_DETECTION_USING_MACHINE_LEARNING_ijariie20360.pdf (Accessed : 25 May 2023).
Harvard Pooja P, Harshith Reddy G, Hrithik K, Devanapalli Nikhil, and Bala Sai Yaswanth Yadav. (2023) 'PHISHING WEBSITE DETECTION USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, 9(3), pp. 1930-1935IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/PHISHING_WEBSITE_DETECTION_USING_MACHINE_LEARNING_ijariie20360.pdf (Accessed : 25 May 2023)
IEEE Pooja P, Harshith Reddy G, Hrithik K, Devanapalli Nikhil, and Bala Sai Yaswanth Yadav, "PHISHING WEBSITE DETECTION USING MACHINE LEARNING," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, pp. 1930-1935, May-Jun 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/PHISHING_WEBSITE_DETECTION_USING_MACHINE_LEARNING_ijariie20360.pdf [Accessed : 25 May 2023].
Turabian Pooja P, Harshith Reddy G, Hrithik K, Devanapalli Nikhil, and Bala Sai Yaswanth Yadav. "PHISHING WEBSITE DETECTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 3 (25 May 2023).
Vancouver Pooja P, Harshith Reddy G, Hrithik K, Devanapalli Nikhil, and Bala Sai Yaswanth Yadav. PHISHING WEBSITE DETECTION USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : 25 May 2023]; 9(3) : 1930-1935. Available from: https://ijariie.com/AdminUploadPdf/PHISHING_WEBSITE_DETECTION_USING_MACHINE_LEARNING_ijariie20360.pdf
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