ABSTRACT Wireless sensors face problems such as poor positioning accuracy in their applications. A hybrid indoor space safety positioning technology is proposed for this purpose. This technology uses genetic models and particle swarm optimization models to optimize the parameters of neural networks. Considering the vulnerability of wireless sensors to Sybil attacks during the positioning process, communication encryption and location awareness are used for optimization. In the positioning experiment, the proposed method had maximum positioning errors of 0.23 m and 0.43 m on the first and second floors, respectively, which were superior to other methods. In the positioning security experiment, the positioning errors of the first and second floors after being attacked by Sybil were 4 m and 5 m respectively, while the proposed security positioning technology had an error range of 1 m. In terms of security comparison, when there are 31–35 attack samples, the proposed method has a maximum root mean square error and average absolute error of 0.72 m and 0.79 m, respectively, which is superior to other models. It can be seen that the proposed technology has excellent positioning effect and stability. The research content will provide important technical support for the practical deployment and security management of wireless sensor networks.