Mr. Weihan Gao Profile

Weihan Gao

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Proceedings Article | 5 June 2024 Paper

Research on security classification for proximity networks with radio access network

Fangjie Chen, Jingpeng Bai, Weihan Gao

Proceedings Volume 13163, 131635U (2024) https://doi.org/10.1117/12.3030564

KEYWORDS: Network security, Information security, Classification systems, Performance modeling, Data modeling, Telecommunication networks, Network architectures

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With the widespread application of 5G networks, next-generation mobile communication systems are actively being studied. In order to solve the problem of difficult coverage of high-frequency signals, researchers have proposed a network architecture called Proximity Network with Radio Access Network (PRAN). Multiple wireless devices are connected to each other through wireless channels, forming a self-organizing network topology. However, the PRAN system currently only judges the security of devices through authorization, which has the drawback of clear risk classification. This article proposes a security level classification method for PRAN, which uses logarithmic functions and network parameters of devices in the PRAN system for security calculation. Then, the security score is calculated based on the coefficients of different roles in the PRAN system. The experimental results show that the method proposed in this paper can achieve a relatively unified security level classification method, effectively distinguishing between normal and abnormal devices, and preventing differences in security levels between different roles.

Proceedings Article | 4 March 2024 Paper

Anomaly detection: research on model selection based on multiple models

Fangjie Chen, Jingpeng Bai, Weihan Gao

Proceedings Volume 12981, 129811P (2024) https://doi.org/10.1117/12.3015099

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Anomal flow detection is an important cornerstone in the field of network security, and has been widely used in various fields. Traditional anomal flow detection schemes are usually based on statistical methods for flow detection. However, with the rapid development of artificial intelligence technology, more and more anomal flow detection schemes are mainly based on machine learning and deep learning, and their application scope is also becoming more and more extensive. Due to the shortcomings of a single model, research on flow detection using multiple models is increasing. Researchers hope to use multiple model schemes to improve the performance of flow detection, but in the use of multiple models, there are many problems such as the number of models and the difficulty of selecting multiple combinations. Based on the F2HDM (Filtering 2-stage Hybrid Detection Method) in multiple model schemes, this paper conducts a mathematical analysis of it, studies model selection issues in different scenarios, proposes a method to calculate the performance of multiple models based on the parameters of a single model, and selects the appropriate combination of anomal flow detection multiple models according to the desired scenario. Finally, we demonstrate through experiments that the selection method proposed in this article has high accuracy.

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