Department of Information Technology, University of the Cumberlands, USA.
Research Article
World Journal of Advanced Research and Reviews, 2022, 14(03), 788-800
Received on 29 April 2022; revised on 08 June 2022; accepted on 23 June 2022
As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Term Memory (LSTM) networks, to detect, classify, and respond to potential security breaches in real-time. The system offers robust malware detection, network traffic analysis, and web intrusion detection while maintaining scalability and efficiency across large cloud environments. Experimental results demonstrate SecureCloudAI's high accuracy, with malware detection at 94.78% and network traffic classification at 90.92%, ensuring the system can handle both complex and emerging threats. This AI-driven solution marks a significant advancement in cloud security, providing organizations with an adaptive and scalable tool to safeguard their data against the ever-growing cyber threat landscape.
SecureCloudAI; Cloud security; Data breaches; AI-driven framework; Machine learning
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Vinay Kumar Kasula, Akhila Reddy Yadulla, Bhargavi Konda and Mounica Yenugula. Enhancing financial cybersecurity: An AI-driven framework for safeguarding digital assets. World Journal of Advanced Research and Reviews, 2022, 14(3), 788-800. Article DOI: https://doi.org/10.30574/wjarr.2022.14.3.1181