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
Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurre...Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents,thereby providing a guarantee for the safety of drivers.However,detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds,varying target scales,and different resolutions.Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios,this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5.The algorithm integrates Attention-based Intra-scale Feature Interaction(AIFI)into the backbone network,enabling it to focus on enhancing feature interactions within the same scale through the attention mechanism.By emphasizing important features,this approach improves detection accuracy,thereby enhancing performance in complex backgrounds.Additionally,a Triple Feature Encoding(TFE)module has been added to the neck network.This module utilizes multi-scale features,encoding and fusing them to create a more detailed and comprehensive feature representation,enhancing object detection and localization,and enabling the algorithm to fully understand the image.Finally,the shape-IoU(Intersection over Union)loss function is adopted to replace the original IoU for more precise bounding box regression.Comparative evaluation of the improved YOLOv5 distraction detection algorithm against the original YOLOv5 algorithm shows an average accuracy improvement of 1.8%,indicating significant advantages in solving distracted driving problems.展开更多
This paper proposes a flexible eight-mode high parallel Galois SIMD ASIP(Application Specific Instruction Set Processor).It supports parallel executions of Gold,Scrambling,CRC,CC,Turbo,RM,PSS,SSS encoding LFSR(linear ...This paper proposes a flexible eight-mode high parallel Galois SIMD ASIP(Application Specific Instruction Set Processor).It supports parallel executions of Gold,Scrambling,CRC,CC,Turbo,RM,PSS,SSS encoding LFSR(linear feedback shift registers)algorithms with high performance and flexibility.It can perform also general bit processing and m-sequence.Our design is based on proposed table conversion and a datapath for unified eight-mode encoding.Based on 28 nm digital CMOS technology,the total area is 0.177 mm2 and the clock frequency can be up to 1 GHz.The throughputs of Gold,Scrambling,CRC32,CRC24,CRC16,CRC8,CC,Turbo are 64 Gb/s,64 Gb/s,128 Gb/s,168 Gb/s,256 Gb/s,512 Gb/s,3×80 Gb/s,and 72 Gb/s,respectively.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.
基金supported by the National Natural Science Foundation of China(62072158,U2004163)the Key Research and Development Special Projects of Henan Province(231111221500)Science and Technology Project of Henan Province(232102210158,242102210197).
文摘Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents,thereby providing a guarantee for the safety of drivers.However,detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds,varying target scales,and different resolutions.Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios,this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5.The algorithm integrates Attention-based Intra-scale Feature Interaction(AIFI)into the backbone network,enabling it to focus on enhancing feature interactions within the same scale through the attention mechanism.By emphasizing important features,this approach improves detection accuracy,thereby enhancing performance in complex backgrounds.Additionally,a Triple Feature Encoding(TFE)module has been added to the neck network.This module utilizes multi-scale features,encoding and fusing them to create a more detailed and comprehensive feature representation,enhancing object detection and localization,and enabling the algorithm to fully understand the image.Finally,the shape-IoU(Intersection over Union)loss function is adopted to replace the original IoU for more precise bounding box regression.Comparative evaluation of the improved YOLOv5 distraction detection algorithm against the original YOLOv5 algorithm shows an average accuracy improvement of 1.8%,indicating significant advantages in solving distracted driving problems.
基金supported in part by the Project of the National Natural Science Foundation of China(Grant No.61961014)supported by the Hainan University project funding KYQD(ZR)1974。
文摘This paper proposes a flexible eight-mode high parallel Galois SIMD ASIP(Application Specific Instruction Set Processor).It supports parallel executions of Gold,Scrambling,CRC,CC,Turbo,RM,PSS,SSS encoding LFSR(linear feedback shift registers)algorithms with high performance and flexibility.It can perform also general bit processing and m-sequence.Our design is based on proposed table conversion and a datapath for unified eight-mode encoding.Based on 28 nm digital CMOS technology,the total area is 0.177 mm2 and the clock frequency can be up to 1 GHz.The throughputs of Gold,Scrambling,CRC32,CRC24,CRC16,CRC8,CC,Turbo are 64 Gb/s,64 Gb/s,128 Gb/s,168 Gb/s,256 Gb/s,512 Gb/s,3×80 Gb/s,and 72 Gb/s,respectively.
