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Article type: Research Article
Authors: Wei, Boa | Chen, Huanyinga; * | Huang, Zhaojib
Affiliations: [a] School of Computer Technol, Henan Quality Polytechnic, Pingdingshan, Henan, China | [b] School of Software, Henan University, Kaifeng, Henan, China
Correspondence: [*] Corresponding author: Huanying Chen, School of Computer Technol, Henan Quality Polytechnic, Pingdingshan 467000, Henan, China. E-mail: [email protected].
Abstract: In order to solve the problem of low accuracy of evaluation results caused by the impact of throughput and transmission delay on traditional systems in 6G networks, this paper proposes a design method of network security processing system in 5G/6gNG-DSS of intelligent model computer. Supported by the principle of active defense, this paper designs a server-side structure, using ScanHome SH-800/400 embedded scanning module barcode QR code scanning device as the scanning engine. We put an evaluation device on the RISC chip PA-RISC microprocessor. Once the system fails, it will send an early warning signal. Through setting control, data, and cooperation interfaces, it can support the information exchange between subsystems. The higher pulse width modulator TL494:4 pin is used to design the power source. We use the top-down data management method to design the system software flow, build a mathematical model, introduce network entropy to weigh the benefits, and realize the system security evaluation. The experimental results show that the highest evaluation accuracy of the system can reach 98%, which can ensure user information security. Conclusion: The problem of active defense network security is transformed into a dynamic analysis problem, which provides an effective decision-making scheme for managers. The system evaluation based on Packet Tracer software has high accuracy and provides important decisions for network security analysis.
Keywords: Active defense, network security assessment, packet tracer, scanning engine, software flow design, mathematical model
DOI: 10.3233/IDT-230143
Journal: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-16, 2023
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