FingerprintGenerator

This generator performs all steps to transform Flow’s into Fingerprint’s. These steps include

  1. Batch data
  2. Clustering (also see Cluster)
  3. Cross correlation (also see CrossCorrelationGraph)
  4. Finding cliques (also see CrossCorrelationGraph)
  5. Transforming cliques into Fingerprints. (also see Fingerprint)
class fingerprints.FingerprintGenerator(batch=300, window=30, correlation=0.1, similarity=0.9)[source]

Generator of FlowPrint Fingerprint objects from flows

batch

Threshold for the batch size in seconds

Type:float
window

Threshold for the window size in seconds

Type:float
correlation

Threshold for the minimum required correlation

Type:float
similarity

Threshold for the minimum required similarity

Type:float
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.

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,)