Improving Cloud Data Transmission Security Through Machine Learning Techniques

Abstract

The rapid adoption of cloud computing has revolutionized data storage and access, but it has also introduced significant challenges in securing data transmission. Traditional security mechanisms, while effective to an extent, are increasingly inadequate in the face of sophisticated cyber threats. This paper explores the integration of machine learning (ML) techniques to enhance the security of data transmission in cloud environments. We propose a hybrid model that employs anomaly detection, encryption optimization, and real-time traffic analysis using supervised and unsupervised ML algorithms. The model dynamically identifies and mitigates potential threats, such as data leakage, man-in-the-middle attacks, and unauthorized access, by learning from historical network patterns and user behavior. Experimental results demonstrate improved accuracy in threat detection and reduced response time compared to conventional methods. The findings highlight the potential of machine learning to provide adaptive, intelligent, and scalable security solutions for cloud data transmission.