51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sn
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import classification_report, confusion_matrix
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from sklearn.model_selection import train_test_split
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def ip_to_bin(x):
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parts = x.split('.')
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return (int(parts[0]) << 24) + (int(parts[1]) << 16) + (int(parts[2]) << 8) + int(parts[3])
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df = pd.read_csv('training4tuplabeled.csv',
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converters={
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'sourceIPAddress': lambda x: ip_to_bin(x),
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'destinationIPAddress': lambda x: ip_to_bin(x)
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})
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df.drop(['flowStartMilliseconds'], 1, inplace=True)
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X = np.array(df.drop(columns=['sublabel']))
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y = np.array(df['sublabel'])
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)
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clf = RandomForestClassifier(n_estimators=50, n_jobs=-1, criterion='gini', random_state=0, class_weight="balanced")
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clf.fit(X_train, y_train)
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accuracy = clf.score(X_test, y_test)
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print('Accuracy: ', accuracy)
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y_pred_train = clf.predict(X_train)
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y_pred_test = clf.predict(X_test)
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print("\n *************** TRAINING ****************")
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cm_train = confusion_matrix(y_train, y_pred_train)
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plt.figure(figsize=(10, 7))
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sn.heatmap(cm_train, annot=True)
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plt.xlabel('Truth')
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plt.ylabel('Predicted')
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plt.show()
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print(classification_report(y_train, y_pred_train))
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print("\n ************** VALIDATION ***************")
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cm_test = confusion_matrix(y_test, y_pred_test)
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plt.figure(figsize=(10, 7))
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sn.heatmap(cm_test, annot=True)
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plt.xlabel('Truth')
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plt.ylabel('Predicted')
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plt.show()
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print(classification_report(y_test, y_pred_test))
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example_measure = np.array([ip_to_bin('2.1.1.1'), ip_to_bin('2.1.1.2'), 0, 0, 1])
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