Paper Title
Subterfuge Sentry: Guarding Against Sneaky Malware Tactics
Article Identifiers
Authors
Ms. Samruddhi Babasaheb Shinde , Dr. S.P.Pawar
Keywords
Abstract
The goal of this project is to create a reliable system that can instantly determine the security status of web links in an era where internet safety is crucial. Support Vector Machines (SVM), Random Forest, Boosting, and Multilayer Perceptron (MLP) are the four different machine learning algorithms that are incorporated into the suggested solution, which makes use of a browser plugin. Every algorithm brings something special to the analysis of several aspects related to connections on the web, offering an all-encompassing assessment of their security. The first step of the project is to gather a variety of datasets with annotated examples of secure and insecure links. These datasets include a wide range of attributes, including URL structure, SSL/TLS certificate details, and historical threat intelligence data. Each algorithm is then trained and optimized using the dataset, maximizing its capacity to correctly categorize links into secure and insecure groups.
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How To Cite (APA)
Ms. Samruddhi Babasaheb Shinde & Dr. S.P.Pawar (May-2024). Subterfuge Sentry: Guarding Against Sneaky Malware Tactics. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), b143-b147. https://ijnrd.org/papers/IJNRD2405122.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : b143-b147
Other Publication Details
Paper Reg. ID: IJNRD_220725
Published Paper Id: IJNRD2405122
Downloads: 000121975
Research Area: Engineering
Country: karad, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405122.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405122
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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