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Title: :  MALWARE WEBSITES DETECTION USING MACHINE LEARNING
PaperId: :  24126
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 3 2024
DUI:    16.0415/IJARIIE-24126
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Nandan MB ATME COLLEGE OF ENGINEERING
SYED UMMER ALMASATME COLLEGE OF ENGINEERING
Zakir HussainATME COLLEGE OF ENGINEERING
Puneeth CATME COLLEGE OF ENGINEERING
Raghuram ASATME COLLEGE OF ENGINEERING

Abstract

Computer Engineering
KNN, SVM, Logistic Regression, XGBoost, and Gradient Boosting.
Phishing attacks have become a prevalent threat in today's digital landscape, posing significant risks to individuals and organizations. To combat this issue, this project presents a novel approach to detecting employing machine learning classification techniques to detect phishing URLs. The primary objective of this project is to develop an efficient and accurate system that can identify phishing URLs with high precision. The proposed solution leverages machine learning algorithms to analyze various features extracted from URLs and make informed predictions regarding their legitimacy. By training the model on a diverse dataset comprising both legitimate and phishing URLs, it learns to differentiate between the two and detect suspicious patterns that indicate potential phishing attempts.The feature extraction process involves capturing several URL characteristics, such as domain name, length, presence of special characters, subdomains, and lexical properties. These features provide valuable insights into the structural and semantic aspects of URLs, which can help distinguish between genuine and malicious links. Additionally, the project explores the use of additional contextual information, such as website reputation, SSL certificate validity, and WHOIS registration details, to enhance the detection accuracy. In this project we are using classification algorithms, namely KNN, SVM, Logistic Regression, XGBoost, and Gradient Boosting, were employed to predict the safety of a given URL. Evaluation of these algorithms was conducted using metrics. Among the algorithms tested, the Gradient Boosting algorithm exhibited superior performance, achieving an accuracy of 97% in correctly identifying phishing URLs. Based on the successful development and evaluation of the A web application was created to give real-time phishing detection using machine learning models. The application accepts input URLs and provides an instant determination of their safety status, assisting users in making informed decisions and protecting their systems from potential harm. The findings of this project demonstrate stress the efficiency of machine learning techniques in spotting phishing URLs the importance of proactive measures to counter phishing attacks. The developed web application holds great potential in enhancing the security posture of individuals and organizations by enabling prompt identification of phishing attempts.

Citations

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IJARIIE Nandan MB , SYED UMMER ALMAS, Zakir Hussain, Puneeth C, and Raghuram AS. "MALWARE WEBSITES DETECTION USING MACHINE LEARNING" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 3 2024 Page 4042-4046
MLA Nandan MB , SYED UMMER ALMAS, Zakir Hussain, Puneeth C, and Raghuram AS. "MALWARE WEBSITES DETECTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 10.3(2024) : 4042-4046.
APA Nandan MB , SYED UMMER ALMAS, Zakir Hussain, Puneeth C, & Raghuram AS. (2024). MALWARE WEBSITES DETECTION USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education, 10(3), 4042-4046.
Chicago Nandan MB , SYED UMMER ALMAS, Zakir Hussain, Puneeth C, and Raghuram AS. "MALWARE WEBSITES DETECTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 3 (2024) : 4042-4046.
Oxford Nandan MB , SYED UMMER ALMAS, Zakir Hussain, Puneeth C, and Raghuram AS. 'MALWARE WEBSITES DETECTION USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, 2024, p. 4042-4046. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/MALWARE_WEBSITES_DETECTION_USING_MACHINE_LEARNING_ijariie24126.pdf (Accessed : ).
Harvard Nandan MB , SYED UMMER ALMAS, Zakir Hussain, Puneeth C, and Raghuram AS. (2024) 'MALWARE WEBSITES DETECTION USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, 10(3), pp. 4042-4046IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/MALWARE_WEBSITES_DETECTION_USING_MACHINE_LEARNING_ijariie24126.pdf (Accessed : )
IEEE Nandan MB , SYED UMMER ALMAS, Zakir Hussain, Puneeth C, and Raghuram AS, "MALWARE WEBSITES DETECTION USING MACHINE LEARNING," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, pp. 4042-4046, May-Jun 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/MALWARE_WEBSITES_DETECTION_USING_MACHINE_LEARNING_ijariie24126.pdf [Accessed : ].
Turabian Nandan MB , SYED UMMER ALMAS, Zakir Hussain, Puneeth C, and Raghuram AS. "MALWARE WEBSITES DETECTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 3 ().
Vancouver Nandan MB , SYED UMMER ALMAS, Zakir Hussain, Puneeth C, and Raghuram AS. MALWARE WEBSITES DETECTION USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(3) : 4042-4046. Available from: https://ijariie.com/AdminUploadPdf/MALWARE_WEBSITES_DETECTION_USING_MACHINE_LEARNING_ijariie24126.pdf
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