Research Trends In Deep Learning and Machine Learning for Cloud Computing Security

Abstract

Meanwhile, security concerns of cloud computing services have grown more diverse and pressurizing with evolution of existing cloud services known to overtake traditional security solutions. Consequently, this paper aims at identifying the contribution of the two most crucial advanced technologies known as machine learning and deep learning in strengthening cloud security. These are techniques that use Artificial Intelligence to detect threats and respond to, or prevent them, automating the identification of abnormalities in cloud traffic. This study focuses on discussing how ML and DL work and their uses, such as fraud detection, real-time authentication in the Zero Trust models, and building security into an application at the software development stage. In addition, it covers important aspects that were previously omitted in other surveys of DL, including privacy, and describes important methods for DL privacy, including federated learning and homomorphic encryption. The paper also explores the issue of model interpretability before highlighting the need for explainable AI frameworks in order to confidence the security administrators. Besides, it explores the threat of adversarial attacks against ML models and also presents a guide to improving model resilience. In conclusion, it is recommended that continuous research should be conducted, and cooperation between researches, practitioners, and policy makers establish to come up with effective and dynamic secure cloud solutions.

Keywords: Machine Learning, Deep Learning, Cloud Computing, Software Development, AI framework, Cloud traffic, Cloud security