fix: replace X_tr by its elements; choose selected samples for training data too
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@ -169,8 +169,8 @@ def create_dataset_from_lists(domains, features, vocab, max_len,
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names.append(np.unique(features[i]['user_hash']))
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servers.append(np.max(features[i]['serverLabel']))
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trusted_hits.append(np.max(features[i]['trustedHits']))
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X = [domain_features, flow_features]
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return X, np.array(hits), np.array(names), np.array(servers), np.array(trusted_hits)
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return (domain_features, flow_features,
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np.array(hits), np.array(names), np.array(servers), np.array(trusted_hits))
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def discretize_label(values, threshold):
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8
main.py
8
main.py
@ -92,7 +92,7 @@ def main():
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user_flow_df = dataset.get_user_flow_data()
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print("create training dataset")
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(X_tr, hits_tr, names_tr, server_tr, trusted_hits_tr) = dataset.create_dataset_from_flows(
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domain_tr, flow_tr, hits_tr, names_tr, server_tr, trusted_hits_tr = dataset.create_dataset_from_flows(
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user_flow_df, char_dict,
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max_len=args.domain_length, window_size=args.window)
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# make client labels discrete with 4 different values
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@ -102,7 +102,9 @@ def main():
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pos_idx = np.where(client_labels == 1.0)[0]
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neg_idx = np.where(client_labels == 0.0)[0]
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idx = np.concatenate((pos_idx, neg_idx))
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# select labels for prediction
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# choose selected sample to train on
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domain_tr = domain_tr[idx]
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flow_tr = flow_tr[idx]
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client_labels = client_labels[idx]
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server_labels = server_tr[idx]
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@ -121,7 +123,7 @@ def main():
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client_labels = np_utils.to_categorical(client_labels, 2)
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server_labels = np_utils.to_categorical(server_labels, 2)
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model.fit(X_tr,
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model.fit([domain_tr, flow_tr],
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[client_labels, server_labels],
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batch_size=args.batch_size,
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epochs=args.epochs,
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