Optimization of computer network security system based on improved neural network algorithm and low energy data search - Miao Zhigang; Cao Ying; Wang Tao (Results in Engineering, 2025)
Traditional network security protection measures often consume high energy when processing massive data, which is difficult to meet the needs of energy conservation and environmental protection. Therefore, this paper aims to optimize the computer network security system, and proposes a method based on improved neural network algorithm and low energy consumption data search to improve security and energy efficiency. This paper analyzes the energy consumption problem in the existing network security architecture, and defines the research goal, that is, to reduce the energy consumption in the process of data search on the premise of ensuring network security. Therefore, this paper uses an improved neural network algorithm to improve the recognition accuracy of network threats. A data search strategy with low energy consumption is designed to reduce the consumption of computing resources by optimizing data access and processing flow. The experimental results show that the proposed method is superior to the traditional method in detecting accuracy and speed, and the energy consumption in the process of data search is greatly reduced. The research shows that the combination of improved neural network algorithm and low-energy search strategy can not only effectively improve the network security protection capability, but also realize the efficient use of resources. Therefore, the low energy data search method based on the improved neural network algorithm provides an effective solution for the optimization of computer network security system, and promotes the development process of information security to energy saving and high efficiency.