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Herein, we proposed a testbed specialized for 5G network data collection that can replay attack data and ensure network security. Additionally, a network collector was designed and implemented to address the limitation due to the tunneling issue when extracting network flows. The purpose of the testbed was to collect real 5G data and build a dataset for AI learning using the collected data. The collected network data can be converted to a labeled dataset using the implemented dataset conversion tool.
We collected network data by performing simulated attacks using an actual 5G user equipment in one smart factory, and by replaying the collected data, we iteratively collected the network data. Using the data from the proposed methodology, we constructed a labeled dataset that can be utilized as learning data for supervised learning methods. In the future, to address the evolving problem of 5G edge cyberattacks, we will develop a 5G security solution for enhancing intelligent network security using the proposed re-collecting approach and various AI technologies
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IEEE/ICACT20210225 Slide.15
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