With the development of computer technology and communication technology, computer network will increasingly become an important means of information exchange, and permeate into every field of social life. However, the network has the potential threat and the reality existence each kind of security question, therefore we must take the strong security policy to ensure the network security. The purpose of this paper is to study the network computer security hidden trouble and vulnerability mining technology. In this paper, the types of security hidden dangers are analyzed, and the vulnerability detection technology Fuzzing technology is deeply studied. Then, the inspection and test time is analyzed for the existing vulnerability detection tools. The experimental results prove that vulnerability detection technology can protect network security with high efficiency of vulnerability detection. In this paper, three vulnerability detection tools WS Fuzzer, Web Fuzz and Webvul Scan were used to analyze the detection time of open source system, personal blog, shopping mall and forum. The average detection time was 1.9s, 8.7s, 20.5s and 59.7s, respectively. It can be seen that the vulnerability mining technology has a certain practical role.

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With the rapid development of semiconductor technology, traditional equation-based modeling faces challenges in accuracy and development time. To overcome these limitations, neural network (NN)-based modeling methods have been proposed. However, the NN-based compact model encounters two major issues. Firstly, it exhibits unphysical behaviors such as un-smoothness and non-monotonicity, which hinder its practical use. Secondly, finding an appropriate NN structure with high accuracy requires expertise and is time-consuming. In this paper, we propose an Automatic Physical-Informed Neural Network (AutoPINN) generation framework to solve these challenges. The framework consists of two parts: the Physics-Informed Neural Network (PINN) and the two-step Automatic Neural Network (AutoNN). The PINN is introduced to resolve unphysical issues by incorporating physical information. The AutoNN assists the PINN in automatically determining an optimal structure without human involvement. We evaluate the proposed AutoPINN framework on the gate-all-around transistor device. The results demonstrate that AutoPINN achieves an error of less than 0.05%. The generalization of our NN is promising, as validated by the test error and the loss landscape. The results demonstrate smoothness in high-order derivatives, and the monotonicity can be well-preserved. We believe that this work has the potential to accelerate the development and simulation process of emerging devices.

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