Research Article
Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic Feature Extraction
| Input: | | | | Output: | | Preprocessed Dataset | (1) | for each n in do | (2) | Split the into | (3) | for each i do | (4) | Purity following “Flow Purification” | (5) | end for | (6) | for each i do | (7) | Cut the length of to MIV bytes | (8) | Traffic data normalization | (9) | Divide the flow into m pieces | (10) | end for | (11) | end for |
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