Enhancing Cyber Fraud Detection in JavaScript Functions through Predictive Analysis with Ensemble Machine Learning Models

Abstract

An important task in the field of cybersecurity is identifying vulnerabilities in JavaScript functions, and this work provides a thorough analysis of the use of ensemble machine learning models for this purpose. The objective of this paper is to improve vulnerability detection in JavaScript code by combining a variety of strong classifiers, such as Random-Forest, Gradient Boosting, and Logistic Regression, into a stacking framework to improve prediction performance and reliability. The technique applies these ensemble models to learn and forecast probable security problems, after a thorough feature engineering process to extract significant patterns from the code. Extensive testing confirms the efficacy of the ensemble method, yielding an astonishing 98% accuracy rate, which is a notable improvement above conventional single-model approach. The finding highlights the promise of ensemble machine learning approaches in the field of cybersecurity, providing developers and security analysts with a viable way to proactively find and fix vulnerabilities in JavaScript applications. The results of this work provide fresh directions for future research on the application of ensemble learning for cybersecurity, in addition to supporting current efforts to secure online applications.