With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due t...With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service(DDoS)attacks across vast IoT devices.Recently,research on automated intrusion detection using machine learning(ML)for 5G environments has been actively conducted.However,5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data.If this data is used to train an ML model,it will likely suffer from generalization errors due to not training enough different features on the attack data.Therefore,this paper aims to study a training method to mitigate the generalization error problem of the ML model that classifies IoT DDoS attacks even under conditions of insufficient and imbalanced 5G traffic.We built a 5G testbed to construct a 5G dataset for training to solve the problem of insufficient data.To solve the imbalance problem,synthetic minority oversampling technique(SMOTE)and generative adversarial network(GAN)-based conditional tabular GAN(CTGAN)of data augmentation were used.The performance of the trained ML models was compared and meaningfully analyzed regarding the generalization error problem.The experimental results showed that CTGAN decreased the accuracy and f1-score compared to the Baseline.Still,regarding the generalization error,the difference between the validation and test results was reduced by at least 1.7 and up to 22.88 times,indicating an improvement in the problem.This result suggests that the ML model training method that utilizes CTGANs to augment attack data for training data in the 5G environment mitigates the generalization error problem.展开更多
第五代移动通信技术(5th Generation Mobile Communication Technology,5G)在智慧交通中的应用正成为现实,为交通管理、车辆通信和安全提供广阔的机会。文章概述5G移动通信技术的优势,总结5G移动通信技术在智慧交通中的应用:在智能交通...第五代移动通信技术(5th Generation Mobile Communication Technology,5G)在智慧交通中的应用正成为现实,为交通管理、车辆通信和安全提供广阔的机会。文章概述5G移动通信技术的优势,总结5G移动通信技术在智慧交通中的应用:在智能交通管理方面,5G移动通信技术可用于实时交通监测与调度、智能交通信号控制、高精度定位与导航等;在车联网和自动驾驶方面,5G移动通信技术可用于车辆间通信、远程监控与控制、自动驾驶系统支持等;在智慧交通安全方面,5G移动通信技术可用于视频监控和事故预警、智能交通地图等。展开更多
基金This work was supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-00796Research on Foundational Technologies for 6GAutonomous Security-by-Design toGuarantee Constant Quality of Security).
文摘With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service(DDoS)attacks across vast IoT devices.Recently,research on automated intrusion detection using machine learning(ML)for 5G environments has been actively conducted.However,5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data.If this data is used to train an ML model,it will likely suffer from generalization errors due to not training enough different features on the attack data.Therefore,this paper aims to study a training method to mitigate the generalization error problem of the ML model that classifies IoT DDoS attacks even under conditions of insufficient and imbalanced 5G traffic.We built a 5G testbed to construct a 5G dataset for training to solve the problem of insufficient data.To solve the imbalance problem,synthetic minority oversampling technique(SMOTE)and generative adversarial network(GAN)-based conditional tabular GAN(CTGAN)of data augmentation were used.The performance of the trained ML models was compared and meaningfully analyzed regarding the generalization error problem.The experimental results showed that CTGAN decreased the accuracy and f1-score compared to the Baseline.Still,regarding the generalization error,the difference between the validation and test results was reduced by at least 1.7 and up to 22.88 times,indicating an improvement in the problem.This result suggests that the ML model training method that utilizes CTGANs to augment attack data for training data in the 5G environment mitigates the generalization error problem.
文摘第五代移动通信技术(5th Generation Mobile Communication Technology,5G)在智慧交通中的应用正成为现实,为交通管理、车辆通信和安全提供广阔的机会。文章概述5G移动通信技术的优势,总结5G移动通信技术在智慧交通中的应用:在智能交通管理方面,5G移动通信技术可用于实时交通监测与调度、智能交通信号控制、高精度定位与导航等;在车联网和自动驾驶方面,5G移动通信技术可用于车辆间通信、远程监控与控制、自动驾驶系统支持等;在智慧交通安全方面,5G移动通信技术可用于视频监控和事故预警、智能交通地图等。
