This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method u...This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.展开更多
为支撑5G-R(5th Generation Mobile Communication Technology for Railway)建设,需构建具备高隔离、高可靠、可管可控特性的新一代承载网络。文章对比分析了切片分组网(SPN)、IP无线接入网(IPRAN)与光传送网(OTN)等3种主流承载技术,并...为支撑5G-R(5th Generation Mobile Communication Technology for Railway)建设,需构建具备高隔离、高可靠、可管可控特性的新一代承载网络。文章对比分析了切片分组网(SPN)、IP无线接入网(IPRAN)与光传送网(OTN)等3种主流承载技术,并通过实验验证及性能测试,评估其在切片隔离性、传输时延、时间同步等方面的表现。研究表明,SPN技术深度融合时分复用与分组交换优势,支持从L1到L3的综合业务承载,具备硬隔离切片、超高精度同步、多业务融合承载等关键能力,能够有效满足铁路5G-R及既有通信业务的高安全、高可靠承载要求,具备良好推广前景。展开更多
基金supported by an Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(RS-2024-00438156,Development of Security Resilience Technology Based on Network Slicing Services in a 5G Specialized Network).
文摘This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.
文摘为支撑5G-R(5th Generation Mobile Communication Technology for Railway)建设,需构建具备高隔离、高可靠、可管可控特性的新一代承载网络。文章对比分析了切片分组网(SPN)、IP无线接入网(IPRAN)与光传送网(OTN)等3种主流承载技术,并通过实验验证及性能测试,评估其在切片隔离性、传输时延、时间同步等方面的表现。研究表明,SPN技术深度融合时分复用与分组交换优势,支持从L1到L3的综合业务承载,具备硬隔离切片、超高精度同步、多业务融合承载等关键能力,能够有效满足铁路5G-R及既有通信业务的高安全、高可靠承载要求,具备良好推广前景。
