Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic Feature Extraction

<div>The left column indicates the three experimental evaluation metrics (accuracy, <i>F</i>1_score, and recall) of random forest selection algorithms for machine learning classifier, and the right column indicates the three experimental evaluation metrics of L1 regularization-based algorithms for classifier.</div>

Computational Intelligence and Neuroscience

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Figure 6

Figure 6: Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic Feature Extraction 