Financial Security

Enhancing Cybersecurity Measures against URL Phishing in E-Banking: A Machine Learning Approach

Abstract:

This paper explores the use of machine learning to improve cybersecurity measures against phishing attacks targeting e-banking platforms. By analysing a comprehensive dataset of phishing and legitimate URLs, machine learning models were developed and evaluated for their effectiveness in detecting phishing threats. This study highlights the potential of using the XGBoost machine learning algorithm in the development of applications with a focus on the identification of malicious URLs. The results of the Phishing URL Detection Model (PUDM) developed in this paper using XGBoost demonstrates a significant enhancement in detection accuracy and response times. An application that includes this model for the identification of malicious URLs will support users using e-banking applications as it will reduce the chances of user's connecting to a malicious URL that will result in the stealing of their sensitive financial information. Using this algorithm in applications will provide proactive defences in the ongoing battle against cyber threats.

Published In

Volume 24, Issue 1

Journal of Information Warfare

The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

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The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

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