FingerprintGenerator¶
This generator performs all steps to transform Flow’s into Fingerprint’s. These steps include
- Batch data
- Clustering (also see Cluster)
- Cross correlation (also see CrossCorrelationGraph)
- Finding cliques (also see CrossCorrelationGraph)
- Transforming cliques into Fingerprints. (also see Fingerprint)
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class
fingerprints.
FingerprintGenerator
(batch=300, window=30, correlation=0.1, similarity=0.9)[source]¶ Generator of FlowPrint Fingerprint objects from flows
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batch
¶ Threshold for the batch size in seconds
Type: float
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window
¶ Threshold for the window size in seconds
Type: float
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correlation
¶ Threshold for the minimum required correlation
Type: float
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similarity
¶ Threshold for the minimum required similarity
Type: float
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FingerprintGenerator.
__init__
(batch=300, window=30, correlation=0.1, similarity=0.9)[source]¶ Generate FlowPrint Fingerprint objects from flows
Parameters: - batch (float, default=300) – Threshold for the batch size in seconds
- window (float, default=30) – Threshold for the window size in seconds
- correlation (float, default=0.1) – Threshold for the minimum required correlation
- similarity (float, default=0.9) – Threshold for the minimum required similarity
Fingerprint generation¶
The method fingerprints.FingerprintGenerator.fit_predict()
performs all steps required for fingerprint generation.
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FingerprintGenerator.
fit_predict
(X, y=None)[source]¶ Create fingerprints from given samples in X.
Parameters: - X (array-like of shape=(n_samples,)) – Samples (Flow objects) from which to generate fingerprints.
- y (array-like of shape=(n_samples,), optional) – Labels corresponding to X. If given, they will be encorporated into each fingerprint.
Returns: result – Resulting fingerprints.
Return type: np.array of shape=(n_samples,)