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Evaluation of Optimization Strategies for Cross border E-commerce Logistics Network Based on adasyn algorithm

Procedia Computer Science 2024年 247卷 1036-1043页

作者： Xiaoxiao Li Jinjiang College Sichuan University Meishan 620860 Sichuan China

With the continuous development of CBEC, logistics networks have become an important and essential component of the e-commerce market. However, in cross-border logistics networks, the complexity and uncertainty of the network often lead to issues such as network imbalance and instability. This article proposes an optimization strategy for cross-border e-commerce (CBEC) logistics networks based on the adasyn algorithm and conducts experimental verification on it. The experimental results show that this strategy can significantly improve the performance indicators of CBEC logistics services. After optimization, logistics time is reduced by 40%; logistics costs are reduced by 40%; delivery efficiency is improved by 8%. In addition, the differences in node quality and distance have also been effectively reduced. This series of optimization measures would provide useful reference and support for the development of CBEC logistics networks.

关键词： adasyn algorithm cross-border e-commerce logistics network optimization machine learning

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Network Intrusion Detection Model Based on PCA + adasyn and XGBoost 2020

Network Intrusion Detection Model Based on PCA + ADASYN and ...

引用

Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science

作者： Leilei Pan Xiaolan Xie Guilin University of Technology Guilin China

ISBN: (纸本)9781450389099

Due to the class-imbalance and redundancy of sample features, the network intrusion detection model based on classification algorithm has high false positive rate (FPR) for minority sample. A network intrusion detection model based on PCA + adasyn and XGBoost is proposed. The principal component analysis (PCA) algorithm is used to reduce the redundancy features of the data. On this basis, the adaptive synthetic sampling (adasyn) algorithm is used to oversample minority sample to solve the problem of class-imbalanced at the data level. Finally, XGBoost is used as a classifier to classify the detected data. In order to verify the validity of the model, several groups of comparative experiments were carried out on KDD CUP99 data set. The FPR of the proposed model for minority samples (r2l, u2r) were 17.3% and 19.7%, and the F1 were 90.1% and 84.5%. The experimental results show that by dealing with the problem of data redundancy and class-imbalanced, we can reduce the FPR of the detection model for minority sample and improve the F1.

关键词： adasyn algorithm XGBoost Class-imbalance Network intrusion detection PCA algorithm

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