Convolutional Machine Learning for Big Data Security: Enhancing Cybersecurity in Cloud Computing Environments

Abstract

In an era marked by exponential data growth and the increasing sophistication of cyber threats, enhancing cybersecurity in cloud computing environments has become a paramount concern. This paper explores the application of convolutional machine learning (CML) techniques as a robust solution for improving big data security. By leveraging CML's ability to process and analyze vast amounts of data efficiently, we demonstrate how these models can enhance threat detection, anomaly identification, and incident response in cloud environments. The study presents a comprehensive framework that integrates CML with existing security measures, focusing on real-time monitoring and proactive defense mechanisms. Experimental results indicate that CML significantly outperforms traditional machine learning approaches in identifying complex attack patterns and reducing false positive rates. Additionally, the adaptive nature of convolutional models enables continuous learning from evolving threats, making them well-suited for dynamic cloud infrastructures. This research contributes to the growing body of knowledge on cybersecurity in big data contexts and provides practical insights for organizations seeking to bolster their defenses against cyber threats.