Enhancing Cloud Computing Security Through Deep Learning: An Artificial Neural Network Approach
Sajeeda Parveen Shaik · INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND APPLIED SCIENCES · 2021
Cloud computing, while offering unparalleled benefits in scalability and efficiency, faces escalating security challenges in the contemporary digital landscape. This paper proposes an innovative approach to fortify cloud computing security through the integration of deep learning, with a specific focus on artificial neural networks (ANNs). By harnessing the adaptive capabilities of ANNs, the study aims to detect and mitigate evolving security threats within diverse cloud environments. The research methodology involves the meticulous selection of neural network architectures, comprehensive training datasets, and rigorous evaluations, including considerations for real-world scenarios and dynamic threat landscapes. Results and analysis showcase the effectiveness of the artificial neural network approach, providing nuanced insights into detection accuracy, false positive rates, and response times under various conditions. Moreover, the paper discusses the potential for transfer learning and ongoing adaptation mechanisms to enhance the robustness of the proposed security framework. This contribution adds significant depth to the discourse on cloud security, offering a detailed roadmap for practitioners and decision-makers seeking advanced, adaptive solutions in the face of increasingly sophisticated and dynamic cyber threats. The integration of deep learning, particularly ANNs, emerges as a promising avenue for elevating the security posture of cloud environments in an ever-evolving digital ecosystem.