Enhancing Cyber Fraud Detection Using Machine Learning Algorithms

Abstract

This research explores the enhancement of cyber fraud detection through the application of machine learning algorithms. As cyber threats continue to evolve in sophistication and frequency, traditional detection methods often fall short in identifying and mitigating fraudulent activities. This study examines various machine learning techniques, including supervised and unsupervised learning models, to improve detection accuracy and response times. By analyzing large datasets from diverse sources, the research identifies key features that contribute to fraudulent behavior, enabling the development of predictive models that can adapt to new patterns. Results indicate that advanced algorithms, such as Random Forest, Gradient Boosting, and Neural Networks, significantly outperform conventional methods in terms of precision, recall, and overall effectiveness.