Cloud computing refers to a high-level network architecture that allows consumers, authorized users, owners, and users to swiftly access and store their data. These days, the user's internal risks have a significant impact on this cloud. An intrusive party is established as a network member and presented as a user. Once they have access to the network, they will attempt to attack or steal confidential information while others are exchanging information or conversing. For the cloud network's external security, there are numerous options. But it's important to deal with internal or insider threats. Thus in the proposed work, an advanced deep learning with optimized missing value imputation is developed to mitigate insider thread in the cloud system. Behavioral log files were taken in an organization which is split into sequential data and standalone data based on the login process. This data was not ready for the detection process due to improper data samples so it was pre-processed using Multivariate Imputation by Chained Equations (MICE) imputation. In this imputation process, the estimation parameter was optimally chosen using the Golf Optimization Algorithm (GOA). After the missing values were filled, the data proceeded to the extraction process. In this, the sequential data are proceeded for the domain extractor and the standalone data are proceeded for Long Short-Term Memory-Autoencoder (LS-AE). Both features are fused to create a single data which is further given to the detection process using Jordan Neural Network (JNN). The proposed method offers 96% accuracy, 92% recall, 91.6% specificity, 8.39% fall out and 8% Miss Rate. The results showed that the recommended JNN detection model has successfully detected insider threads in a cloud system.