Enhancing Cloud Security with Deep Learning-Based Intrusion Detection in Cloud Computing Environments

Cloud computing (CC) offers a wide range of on-demand resources and services over the internet. However, due to the inherent vulnerability of the cloud's dispersed architecture, guaranteeing the privacy and security of cloud assets and resources remains a tough task. Data and service protection in cloud systems is a continuous and serious concern. This article presents a fresh solution to this problem by utilizing Deep Learning (DL) techniques for intrusion detection in cloud computing systems. The present research analyses the strategies used by several Intrusion Detection Systems (IDS). As network infrastructures expand, so do security risks, increasing the need for dependable intrusion detection. Developers have created numerous intrusion detection systems (IDS) in response to the expanding security and privacy issues that saturate modern computer networks. Improving the datasets used for training and testing these security solutions requires equal focus as building defense systems. Improved datasets significantly enhance the detection capabilities of both offline and online intrusion detection models. This essay contributes to the growing corpus of research on CC security by conducting a thorough examination of publicly available network-based IDS datasets. It emphasizes the need to incorporate cutting-edge DL approaches into IDS for increased security. As cloud computing continues to transform the digital world, the findings of this study have major implications for safeguarding sensitive data and key services in cloud-based ecosystems. Furthermore, they provide solutions to the current anomaly detection issues caused by insufficient normal patterns in training data. The detection of accuracy information for two, five, and twenty-three classes and soft-max regression (SMR) feature learning perform similarly for 5-class and 23-class categorization, and it was also found that STL completed all categorization categories with above 98% accuracy.