MESSAI.iO

AI summary

50% confidence

This paper proposes a machine learning-based approach for classifying SSH applications, leveraging the MFC framework to improve classification accuracy.

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Extraction

Reported parameters

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Key findings

  • The proposed approach achieved an accuracy of 95% in classifying SSH applications using a combination of feature extraction and machine learning algorithms.
  • The MFC framework was found to be effective in reducing the dimensionality of the feature space, improving the performance of the classification model.
  • The results showed that the proposed approach outperformed traditional classification methods, such as decision trees and random forests, in terms of accuracy and efficiency.

Keywords

Artificial intelligenceMachine learning

Identifiers

DOI
10.38007/ml.2021.020303
Journal
Machine Learning Theory and Practice
Year
2021