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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Malicious URL Identification Using Machine Learning - Brosaf |
| Authors Name: | Omkar Pandurang Parab , Nishad Mahendra Patil , Vidhi Lalit Patil , Dr. Biplab Sarkar |
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IJRTI_186155
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| Published Paper Id: | IJRTI2304159 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Concerns about the security of web material, particularly URLs, rise along with the use of the network. For this assignment, we created a website that calculates the percentage of safe URLs based on machine learning algorithms. On our website, we have implemented a pipeline that pre-processes URLs, extracts their features, and then subjects them to a machine learning model that has been tuned using a collection of tagged URLs. We tested several feature extraction techniques, such as domain, HTTPS protocol, and URL length, and we evaluated using measures like precision, accuracy, recall, and F1 score Our website provides a user-friendly interface that allows users to enter a URL and obtain an immediate safety assessment in the form of a percentage. The percentage reflects the likelihood of the URL being safe, based on our machine learning model's prediction. We evaluated the performance of our website using a set of URLs. The experimental evaluation revealed that our website had a high F1 score and accuracy and could provide trustworthy safety assessments for a wide variety of URLs. Overall, our project emphasizes the potential of this methodology for improving user awareness and online security while demonstrating the efficacy of machine learning techniques for URL safety analysis. Individuals, businesses, and internet service providers can use our website to evaluate the security of URLs and guard against malicious material |
| Keywords: | Machine learning, dataset, pre-processes, F1 score, URL, evaluation, online security. |
| Cite Article: | "Malicious URL Identification Using Machine Learning - Brosaf", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.978 - 982, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304159.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2304159
Registration ID:186155
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 978 - 982 Country: Thane, Maharashtra, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2304159 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2304159 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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