Personalized Arrhythmia Detection Based on Lightweight Autoencoder and Variational Autoencoder

Zhong, Zhaoyi; Sun, Le; Subramani, Sudha

doi:10.1007/978-3-031-15512-3_4

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…In terms of data, Zhang et al [26] proposed a method to generate samples based on the variational autoencoder generation model to balance the dataset. Te core idea is to expand only boundary samples, which are most likely to cause confusion to machine learning when expanding minority samples.…”

Section: Balanced Datasetmentioning

confidence: 99%

Network Traffic Classification Based on SD Sampling and Hierarchical Ensemble Learning

Qin,

Han,

Wang

et al. 2023

Security and Communication Networks

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With the increase in cyber threats in recent years, there have been more forms of demand for network security protection measures. Network traffic classification technology is used to adapt to the dynamic threat environment. However, network traffic has a natural unbalanced class distribution problem, and the single model leads to the low accuracy and high false-positive rate of the traditional detection model. Given the above two problems, this paper proposes a new dataset balancing method named SD sampling based on the SMOTE algorithm. Different from the SMOTE algorithm, this method divides the sample into two types that are easy and difficult to classify and only balances the difficult-to-classify sample, which not only overcomes the SMOTE’s overgeneralization but also combines the idea of oversampling and undersampling. In addition, a two-layer structure combined with XGBoost and the random forest is proposed for multiclassification of anomalous traffic, since using a hierarchical structure can better classify minority abnormal traffic. This paper conducts experiments on the CICIDS2017 dataset. The results show that the classification accuracy of the proposed model is more than 99.70% and that the false-positive rate is less than 0.34%, indicating that the proposed model is better than traditional models.

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Section: Balanced Datasetmentioning

confidence: 99%