Few-Shot network intrusion detection based on prototypical capsule network with attention mechanism

Sun, Handi; Wan, Liang; Liu, Mengying; Wang, Bo

doi:10.1371/journal.pone.0284632

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…The proposed method was evaluated using a rolling bearing experimental dataset and a motor fault dataset to assess its performance and validate its effectiveness. Sun, H. et al [18] presented an intrusion detection method for capsule networks that utilizes an attention mechanism. This method aims to tackle the issue of imbalanced sample data.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

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The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

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“…These various measures all convey different information about a classification, each with merits and demerits for a particular application. Consequently, there have been many calls for the use of multiple metrics although this can complicate interpretation [ 13 , 14 ]. Additionally, a common, albeit unrealistic, desire is to have a single value to summarise a binary classification and recent literature has promoted the Matthews correlation coefficient (MCC) for all researchers and all subjects [ 11 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient

Foody

2023

PLoS ONE

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The accuracy of a classification is fundamental to its interpretation, use and ultimately decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the true accuracy. Mis-estimation of classification accuracy metrics and associated mis-interpretations are often due to variations in prevalence and the use of an imperfect reference standard. The fundamental issues underlying the problems associated with variations in prevalence and reference standard quality are revisited here for binary classifications with particular attention focused on the use of the Matthews correlation coefficient (MCC). A key attribute claimed of the MCC is that a high value can only be attained when the classification performed well on both classes in a binary classification. However, it is shown here that the apparent magnitude of a set of popular accuracy metrics used in fields such as computer science medicine and environmental science (Recall, Precision, Specificity, Negative Predictive Value, J, F1, likelihood ratios and MCC) and one key attribute (prevalence) were all influenced greatly by variations in prevalence and use of an imperfect reference standard. Simulations using realistic values for data quality in applications such as remote sensing showed each metric varied over the range of possible prevalence and at differing levels of reference standard quality. The direction and magnitude of accuracy metric mis-estimation were a function of prevalence and the size and nature of the imperfections in the reference standard. It was evident that the apparent MCC could be substantially under- or over-estimated. Additionally, a high apparent MCC arose from an unquestionably poor classification. As with some other metrics of accuracy, the utility of the MCC may be overstated and apparent values need to be interpreted with caution. Apparent accuracy and prevalence values can be mis-leading and calls for the issues to be recognised and addressed should be heeded.

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ASAP: Automatic Synthesis of Attack Prototypes, an online-learning, end-to-end approach

Cevallos M.,

Rizzardi,

Sicari

et al. 2024

Computer Networks

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Few-Shot network intrusion detection based on prototypical capsule network with attention mechanism

Cited by 5 publications

References 33 publications

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient

ASAP: Automatic Synthesis of Attack Prototypes, an online-learning, end-to-end approach

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