Article
ENHANCING DATA TRANSMISSION SECURITY IN CLOUD USING MACHINE LEARNING
Cloud computing has become the backbone of modern digital infrastructure, enabling scalable storage and seamless data transmission across distributed networks. However, the increasing volume of sensitive data transmitted over cloud platforms has made them prime targets for cyber-attacks such as data interception, Distributed Denial of Service (DDoS), and advanced persistent threats (APTs). Traditional security mechanisms, including static encryption protocols and signature-based intrusion detection systems, are insufficient in addressing dynamic and evolving threats. This paper proposes an advanced machine learning-based framework for enhancing data transmission security in cloud environments. The system integrates supervised and unsupervised learning models for anomaly detection, real-time traffic analysis, and adaptive encryption mechanisms. A hybrid approach combining Random Forest, Support Vector Machine, and Neural Networks is utilized to improve detection accuracy and reduce false positives. The system is evaluated using benchmark datasets such as NSL-KDD, achieving an accuracy of up to 98%. The proposed model demonstrates improved resilience, scalability, and adaptability compared to conventional approaches.
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