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Network Traffic Synthesis and Simulation Framework for Cybersecurity Exercise Systems

Computers, Materials & Continua 2024年第9期80卷 3637-3653页

作者： Dong-Wook Kim Gun-Yoon Sin Kwangsoo Kim jaesik kang Sun-Young Im Myung-Mook Han Department of AI Software Gachon UniversitySeongnam-Si13120Republic of Korea School of Computer Engineering&Applied Mathematics Hankyong National UniversityPyeongtaek-Si17738Republic of Korea Cyber ElectronicWarfare Research and Development LIG Nex1Seongnam-Si13488Republic of Korea

In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly *** methods often fall short in capturing the dynamic and complex nature of modern cyber *** address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise *** framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the *** cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data *** technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network *** meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual *** integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic *** simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our *** initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and *** providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity *** not only improves their ability to r

关键词： Cybersecurity exercise synthetic network traffic generative adversarial network traffic generation software-defined networking

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