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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Gupta, Himanshu
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Computer Science & IT Electrical & Electronics Engineering Engineering

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A machine learning model for predicting phishing websites Odette Boussi, Grace; Gupta, Himanshu; Hossain, Syed Akhter
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4228-4238

Abstract

There are various types of cybercrime, and hackers often target specific ones for different reasons, such as financial gain, recognition, or even revenge. Cybercrimes are not restricted by geographical boundaries and can occur globally. The prevalence of specific types of cybercrime can vary from country to country, influenced by factors such as economic conditions, internet usage levels, and overall development. Phishing is a common cybercrime in the financial sector across different countries, with variations in techniques between developed and developing nations. However, the impact, often leading to financial losses, remains consistent. In our analysis, we utilized a dataset featuring 48 attributes from 5,000 phishing webpages and 5,000 legitimate webpages to predict the phishing status of websites. This approach achieved an impressive 98% accuracy.
Enhancing financial cybersecurity via advanced machine learning: analysis, comparison Odette Boussi, Grace; Gupta, Himanshu; Hossain, Syed Akhter
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1281-1289

Abstract

The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper looks at using advanced machine learning methods to make cybersecurity stronger for financial institutions. The work explores the deployment of cutting-edge machine learning algorithms - logistic regression, random forest, support vector machines (SVM), K-nearest neighbour (KNN), naïve Bayes, extreme gradient boosting (XGBoost), and deep learning technique (Dense Layer) - to fortify the cybersecurity framework within financial institutions. Through a meticulous analysis and comparative study, we explore the efficacy, scalability, and practical implementation aspects of various machine learning algorithms tailored to address cybersecurity concerns. Additionally, we propose a framework for integrating the most effective machine learning models into existing cybersecurity infrastructure, offering insights into bolstering resilience against evolving cyber threats. In our comparison, XGBoost exhibited outstanding performance with an accuracy of 95%.
Co-Authors Hossain, Syed Akhter Odette Boussi, Grace