Abstract
In view of the network security risks caused by the integration of the Industrial Internet of Things (IIoT) in the construction of smart cities, this research proposes a digital security identification model (DL-DSIM) based on deep learning, which aims to improve the data transmission efficiency and system security in the smart city environment. With the widespread application of advanced technologies in smart cities, the rapid development of IIoT has become an important force in promoting its evolution, but at the same time, the expansion of the attack surface has also brought many security risks. In order to deal with these problems, this paper designs a flexible three-layer architecture framework, and introduces a new intrusion detection feature selection method that combines flock optimization (CSO) and genetic algorithm (GA) to reduce the complexity of feature selection and enhance the detection and processing power of security vulnerabilities through deep neural networks (DNN). The simulation experiment results show that DL-DSIM achieved 99.13% accuracy, 98.5% recall rate, 98.39% F value, 99.16% accuracy and 95.62% specificity in the training phase, and also achieved 96.1% accuracy, 95.48% recall rate, 96.38% F value, 95.89% accuracy and 93% specificity in the test phase. These achievements fully reflect the efficiency of DL-DSIM in resisting network security threats, provide a reliable security mechanism for IIoT systems in smart cities, and further promote the sustainable development of smart cities and the construction of digital security.