Abstract: Integrating cloud computing and deep learning for cybersecurity is a promising area of research and application, given the increasing complexity and volume of cyber threats. This article examines the current state of play and emerging trends in combining deep learning and cloud computing, as well as how these two technologies interact. The global public cloud services market is growing at a rapid pace, which makes data management more vulnerable to cyberattacks and breaches. To increase intrusion detection's efficacy in cloud computing environments, various intrusion detection systems employ various deep learning techniques. The security of cloud data is further enhanced by the application of encryption technology and the related deep learning retrieval technology. Furthermore, by effectively allocating resources and resolving the issue of slow cloud service speed, the paper thoroughly examines how the deep reinforcement learning scheduling mechanism can optimize cloud service performance. To solve the issues with energy consumption in cloud computing data centers, it also determines the best energy plan using deep neural networks. In addition, the five new cloud computing architectures are reviewed, and the function of deep learning in these frameworks is examined. Lastly, it examines some of the issues that cloud computing and deep learning may face in the future, such as low latency and high throughput optimization in deep learning and cloud computing security and confidentiality. In conclusion, this article sheds light on the patterns, obstacles, and possibilities for the future development of deep learning and cloud computing integration.

Keywords: Security, Deep learning, Cloud computing, Cybersecurity.

| DOI: 10.17148/IMRJR.2024.010104