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files with ipynb extension need to be open with jupyter notebook, and contains some part of code not in the python files. Dataset or the pcap file used is obtained from this link: https://iotanalytics.unsw.edu.au/iottraces
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import matplotlib.pyplot as plt | ||
import matplotlib.cm as cm | ||
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from sklearn.metrics import plot_confusion_matrix | ||
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mac_to_device = {'00:16:6c:ab:6b:88':'Samsung SmartCam', '00:24:e4:11:18:a8':'Withings Smart Baby Monitor', '00:24:e4:1b:6f:96':'Withings Smart scale', | ||
'08:21:ef:3b:fc:e3':'Samsung Galaxy Tab', '14:cc:20:51:33:ea':'TPLink Router', '18:b4:30:25:be:e4':'NEST Protect smoke alarm', | ||
'18:b7:9e:02:20:44':'Triby Speaker', '30:8c:fb:2f:e4:b2':'Dropcam','44:65:0d:56:cc:d3':'Amazon Echo', | ||
'50:c7:bf:00:56:39':'TP-Link Smart plug', '70:5a:0f:e4:9b:c0':'HP Printer', '70:ee:50:03:b8:ac':'Netatmo weather station', | ||
'70:ee:50:18:34:43':'Netatmo Welcome', 'd0:52:a8:00:67:5e':'Smart Things', 'e0:76:d0:33:bb:85':'PIX-STAR Photo-frame', | ||
'ec:1a:59:79:f4:89':'Belkin Wemo switch', 'ec:1a:59:83:28:11':'Belkin wemo motion sensor', 'f4:f2:6d:93:51:f1':'TP-Link Day Night Cloud camera', | ||
} | ||
devices = [d for d in mac_to_device.values()] | ||
plot_confusion_matrix(classifier, X_test, y1_test, normalize='pred', display_labels=devices, include_values=True) | ||
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########### Y2 ############## | ||
plot_confusion_matrix(classifier, X_test, y1_test, normalize='pred') | ||
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CMd = pd.DataFrame(CM, columns=devices, index=le1.classes_) | ||
CMd.head(18) | ||
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classifier.feature_importances_ | ||
classifier.score(X_test,y_test) | ||
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classifier.predict_proba(X_test)[0:33] | ||
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from sklearn.model_selection import cross_val_score | ||
import numpy as np | ||
cv = cross_val_score(classifier, X_train, y_train, cv=10) | ||
print(cv) | ||
print(np.mean(cv)) | ||
# scores | ||
print("CV") | ||
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from sklearn.metrics import confusion_matrix | ||
from sklearn.metrics import accuracy_score | ||
from sklearn.metrics import classification_report | ||
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print ('Confusion Matrix :') | ||
CM = confusion_matrix(y_test, predicted, normalize="pred") | ||
CM_not = confusion_matrix(y_test,predicted) | ||
print(CM) | ||
# print('le1 classes:', le1.classes_) | ||
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print ('Accuracy Score :',accuracy_score(y_test, predicted)) | ||
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print ('Report : ') | ||
print (classification_report(y_test, predicted)) | ||
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# X_train, X_test, y2_train, y2_test = train_test_split(X, y2, test_size=0.3, random_state=0) | ||
# classifier2 = RandomForestClassifier() | ||
# classifier2.fit(X_train, y2_train) | ||
# predicted2 = classifier2.predict(X_test) | ||
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# print ('Confusion Matrix :') | ||
# print(confusion_matrix(y2_test, predicted2)) | ||
# print ('Accuracy Score :',accuracy_score(y2_test, predicted2)) | ||
# print ('Report : ') | ||
# print (classification_report(y2_test, predicted2)) | ||
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%matplotlib inline | ||
import seaborn as sn | ||
plt.figure(figsize=(18,9)) | ||
sn.heatmap(CM, annot=True) | ||
plt.xlabel('Predicted') | ||
plt.ylabel('Truth') | ||
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import pandas as pd# CMd = pd.DataFrame(CM, columns=devices, index=le1.classes_) | ||
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CMd = pd.DataFrame(CM_not, columns=devices, index=devices) | ||
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CMd.head(18) | ||
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from sklearn.metrics import precision_recall_fscore_support | ||
precision, recall, f1Score, support = precision_recall_fscore_support(y_test, predicted) | ||
print(precision, recall, f1Score, support) | ||
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fig = plt.figure() | ||
ax = fig.add_subplot(111) | ||
plt.scatter(f1Score, support) | ||
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from sklearn.metrics import balanced_accuracy_score | ||
balanced_accuracy_score(y_test, predicted) | ||
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