Market behavior-oriented deep learning-based secure data analysis in smart cities

The construction of Smart Cities is inseparable from the healthy operation of markets. Reasonable data analysis can provide a crucial foundation for the development of market behavior by considering the enormous amount of data generated by a market economy. To this end, we propose enhanced cluster generative adversarial networks (eClusterGAN) to achieve latent space clustering. However, data storage security is crucial. Moreover, we suggest a GAN-based network intrusion detection system (GAN-NIDS) that uses adversarial learning to assist the generator in learning the spatial distribution of normal network flows. The simulation results showed that the proposed eClusterGAN and GAN-NIDS outperformed the benchmarks in terms of clustering accuracy, running time, precision, recall, and F1, which can support researchers in studying economic data trends. The construction of Smart Cities can effectively ensure healthy market development by discovering and disseminating the potential value of market economic data.

Automated summary

The construction of Smart Cities is closely related to the healthy operation of markets. Reasonable data analysis plays a crucial role in market behavior development, considering the vast amount of data generated by a market economy. We propose enhanced cluster generative adversarial networks (eClusterGAN) for latent space clustering and suggest a GAN-based network intrusion detection system (GAN-NIDS) that uses adversarial learning to learn the spatial distribution of normal network flows. Simulation results demonstrate that the proposed eClusterGAN and GAN-NIDS outperform benchmarks in terms of clustering accuracy, running time, precision, recall, and F1, supporting researchers in studying economic data trends. The construction of Smart Cities can effectively ensure healthy market development by discovering and disseminating the potential value of market economic data.