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Statistics report
Aug. 2022
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 113
Paper Published : 1372
No. of Authors : 3469
  Journal Paper


Paper Title :
Predicting Fraudulent URLs through Machine Learning

Author :Ekansh Trivedi

Article Citation :Ekansh Trivedi , (2021 ) " Predicting Fraudulent URLs through Machine Learning " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 17-20, Volume-9,Issue-12

Abstract : Machine Learning has proved to be pivotal and efficient in predicting relative outcomes of events and helping people take more secure and informed decisions. Such decisions have been important in almost every field that has a relation with information. One such field is CyberSecurity, and through this project, we will be focusing on Predicting the type of Uniform Resource Locator‟s (URL) that tend to be fraudulent in nature and can prove to be harmful in the near future as everything we know is going digital. This will be done by comparing Machine Learning techniques such as Random Forest, Decision Tree, Multi-Layer Perceptron, Logistic Regression, and XGboost. Furthermore, the features of a URL will be explained through visualizations and how they contribute to predicting fraudulent URLs. Keywords - Cyber Security, Machine Learning and Phishing etc.

Type : Research paper

Published : Volume-9,Issue-12


DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-18290   View Here

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