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attackVTCAS.py
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import pickle
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
from sklearn import preprocessing
from sklearn.preprocessing import Normalizer
import pandas as pd
def parse_demographics():
df = pd.read_csv("./Demographics.csv", usecols=['Gender'])
result = []
ctr = 0
for index, row in df.iterrows():
if (row["Gender"] == "M"):
result.append(1)
else:
result.append(0)
return result
def attack(pkl, model):
x = unpickling(pkl)
x = scaler.transform(x)
x = transformer.transform(x)
model = unpickling(model)
y = [0]*len(x)
y_pred = model.predict(x)
far, frr, hter = HTER(y, y_pred)
'''print ("Accuracy:", accuracy_score(y, y_pred))
print ("FAR:", far)
print ("FRR", frr)
print ("HTER", hter)'''
return accuracy_score(y, y_pred), far, frr, hter
def attack3(data, model):
data = unpickling(data)
X = []
y = []
for i in data:
X += data[i]
y += [0]*len(data[i])
X1, y = create_sliding_window(X, y, 5)
X1 = np.array(X1)
sc = StandardScaler()
X1 = sc.fit_transform(X1)
transformer = Normalizer().fit(X1)
transformer.transform(X1)
model = unpickling(model)
y = np.array(y)
y_pred = model.predict(X1)
far, frr, hter = HTER(y, y_pred)
'''print ("Accuracy:", accuracy_score(y, y_pred))
print ("FAR:", far)
print ("FRR", frr)
print ("HTER", hter)'''
return accuracy_score(y, y_pred), far, frr, hter
def attack2(X, u, user_id, model):
y = binarize(u, user_id)
X1, y = create_sliding_window(X.tolist(), y, 5)
X1 = np.array(X1)
sc = StandardScaler()
X1 = sc.fit_transform(X1)
transformer = Normalizer().fit(X1)
transformer.transform(X1)
model = unpickling(model)
y = np.array(y)
X1 = X1[np.where(y==1)]
y = y[np.where(y==1)]
y_pred = model.predict(X1)
far, frr, hter = HTER(y, y_pred)
'''print ("Accuracy:", accuracy_score(y, y_pred))
print ("FAR:", far)
print ("FRR", frr)
print ("HTER", hter)'''
return accuracy_score(y, y_pred), far, frr, hter
def binarize(u, id):
ans = []
for i in u:
if int(i) == int(id):
ans.append(1)
else:
ans.append(0)
return ans
def create_sliding_window(X, Y, n):
final_X = []
final_Y = []
for i in range(len(X)- n):
temp = []
for j in range(i, i + n):
temp += X[j]
final_X.append(temp)
final_Y.append(Y[i+n])
return final_X, final_Y
def HTER(y, pred):
'''
Params: (Expected Binary Labels)
y: Original Labels
pred: Predicted Labels
Returns:
--------------
FAR, FRR, HTER respectively
'''
far = frr = 0
for i in range(len(y)):
if y[i] == 0 and pred[i] == 1:
far += 1
if y[i] == 1 and pred[i] == 0:
frr += 1
far /= len(y)
frr /= len(y)
hter = (frr + far)/2
return far, frr, hter
def pickling(fname, obj):
f = open(fname, "wb")
pickle.dump(obj, f)
f.close()
def unpickling(fname):
f = open(fname, 'rb')
g = pickle.load(f)
f.close()
return g
u = unpickling("Pickle_Files_Swipe/Ids_U.pkl")
u = np.array(u)
X = unpickling("Pickle_Files_Swipe/features_X.pkl")
X = np.array(X)
scaler = unpickling("scaler_Vanilla_phone.pkl")
transformer = unpickling("transformer_Vanilla_phone.pkl")
gender_list = parse_demographics()
file_ptr = open("Vanilla_Attack_Pop.out", "w")
models = ["SVM", "RForest", "MLP", "XGBoost"]
for model in models:
final_acc = []
final_far = []
final_frr = []
final_hter = []
final_acc_m = []
final_far_m = []
final_frr_m = []
final_hter_m = []
final_acc_f = []
final_far_f = []
final_frr_f = []
final_hter_f = []
for i in range(1,117):
#acc, far, frr, hter = attack3("umdaa_features.pkl",str(model)+"_"+str(i)+"Vanilla_Phone_Vanilla.pkl")
#acc, far, frr, hter = attack3("serwadda_features.pkl",str(model)+"_"+str(i)+"Vanilla_Phone_Vanilla.pkl")
#acc, far, frr, hter = attack3("random_attack_data.pkl",str(model)+"_"+str(i)+"Vanilla_Phone_Vanilla.pkl")
#acc, far, frr, hter = attack3("hmog_features.pkl",str(model)+"_"+str(i)+"Vanilla_Phone_Vanilla.pkl")
acc, far, frr, hter = attack("pop_data_10000.pkl",str(model)+"_"+str(i)+"Vanilla_Phone_Vanilla.pkl")
final_acc.append(acc)
final_far.append(far)
final_frr.append(frr)
final_hter.append(hter)
if (gender_list[i] == 1):
final_acc_m.append(acc)
final_far_m.append(far)
final_frr_m.append(frr)
final_hter_m.append(hter)
file_ptr.write("{},{},{},{},{}\n".format(acc,far,frr,hter,"M"))
else:
final_acc_f.append(acc)
final_far_f.append(far)
final_frr_f.append(frr)
final_hter_f.append(hter)
file_ptr.write("{},{},{},{},{}\n".format(acc,far,frr,hter,"F"))
file_ptr.write("#####################"+str(model)+"##################### \n")
file_ptr.write("Final Acc:"+str(np.mean(final_acc))+"\n")
file_ptr.write("Final FAR:"+str(np.mean(final_far))+"\n")
file_ptr.write("Final FRR:"+str(np.mean(final_frr))+"\n")
file_ptr.write("Final HTER:"+str(np.mean(final_hter))+"\n")
file_ptr.write("Accuracy Male:"+str(np.mean(final_acc_m))+"\n")
file_ptr.write("FAR Male:"+str(np.mean(final_far_m))+"\n")
file_ptr.write("FRR Male:"+str(np.mean(final_frr_m))+"\n")
file_ptr.write("HTER Male:"+str(np.mean(final_hter_m))+"\n")
file_ptr.write("ACC Female:"+str(np.mean(final_acc_f))+"\n")
file_ptr.write("FAR Female:"+str(np.mean(final_far_f))+"\n")
file_ptr.write("FRR Female:"+str(np.mean(final_frr_f))+"\n")
file_ptr.write("HTER Female:"+str(np.mean(final_hter_f))+"\n")
file_ptr.write("########################################## \n")