Robust Framework for Malevolent URL Detection using Hybrid Supervised Learning

Document Type

Conference Proceeding

Publication Title

Procedia Computer Science

Abstract

The In this Internet Era, most of the people are using world-wide-web (www) based websites extensively for accomplishing their daily activities including E-commerce. As per latest report, total of 1.5 billion URLs are accessed every day and it is increasing every second. From another perspective, malicious URLs are employed in URL phishing- which in turn steal customer credentials and thereby lead to loss of billions of dollars. Due to these aspects, malevolent URL detection is gaining huge attention in the literature in the past few years. However, the state-of-the art literature employ only popular ML techniques and small datasets with less diversity for detecting malicious URLs. Further, the existing malicious URL detection methods are less focusing on bottleneck issues such as Overfitting and hyper-parameter tuning of ML models, which play a significant role in deciding the prediction accuracy of the proposed detection framework. In order to tackle these issues, this research article proposes a robust Malevolent URL Detection framework, which utilizes a hybrid machine learning algorithm, Support Vector Regression (SVR) and hyper parameters tuning strategies to enhance the prediction accuracy. The experimental results conducted on training and testing datasets clearly demonstrate the performance of the proposed detection framework in terms of PR and accuracy metrics respectively.

First Page

241

Last Page

247

DOI

10.1016/j.procs.2023.12.079

Publication Date

1-1-2023

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