Hybrid GAN-Based Transfer Learning Model for Advanced Threat Detection in Cloud Computing

In the realm of cloud computing, reliable detection of threats forms a very important aspect since systems might be at risk. In this research, they proposed a new model that incorporates GANs with transfer learning to allow for the development of accurate detection systems that can identify even the more concealed forms of hacks that are yet to be recognized. Data gathering encompasses compiling a large number of heterogeneous datasets, including historical attack data, network traffic logs, UNSW-NB15, and CICIDS, as well as those obtained from cloud service providers. A novel approach is then proposed that combines both steps: The first step involves the generation of synthetic data through GANs, and the second step involves improving the feature representation of the same. The utilization of the hybrid model has been demonstrated to have an astounding accuracy of 98% when it comes to the capability of recognizing both classic and new types of attacks, as indicated by the assessments. According to the comparative analysis of the results, the proposed hybrid approach that was provided has a higher level of efficiency compared to traditional threat detection systems while also having increased adaptability in different scenarios of cloud computing. This work not only makes a contribution to the improvement of cloud threat detection, but it also offers insights that may be used for the later development and applications of threat detection in a variety of cloud environments.