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from tqdm import tqdm
import torch
from substituteModel.DNN import DNN
from utils.CICIDSData import CICIDS
from torch.utils.data import DataLoader
from TargetModel.TargetLR import TargetLR
from TargetModel.TargetIF import TargetIF
from sklearn.metrics import confusion_matrix
from TargetModel.TargetDT import TargetDT
from TargetModel.TargetSVM import TargetSVM
import numpy as np
def getPredDataDL(dataloader, model, device, filename):
y_pred = []
origin_X = []
for batch_x, batch_y in tqdm(dataloader):
origin_X += batch_x.detach().cpu().numpy().tolist()
batch_x = batch_x.to(device)
output = model(batch_x)
y_pred += torch.argmax(output, dim=1).detach().cpu().numpy().tolist()
y_pred = np.array([[x] for x in y_pred])
origin_X = np.array(origin_X)
data = np.hstack((origin_X, y_pred))
print(data.shape)
np.save(filename, data)
def getPredDataML(dataloader, model, filename):
X = []
y = []
for batch_x, batch_y in dataloader:
X += batch_x.data.numpy().tolist()
y += batch_y.data.numpy().tolist()
X = np.array(X)
y = np.array(y)
y_pred = model.clf.predict(X)
if 'if' in filename:
y_pred[y_pred == -1] = 0
y_pred[y_pred == 1] = 1
print(confusion_matrix(y, y_pred))
y_pred = np.array([[x] for x in y_pred])
data = np.hstack((X, y_pred))
print(data.shape)
np.save(filename, data)
if __name__ == '__main__':
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
proxy_model_file = "/home/sunhanwu/work2021/TrafficAdversarial/experiment/src/modelFile/proxy_cicids_dnn_Fuzzing.pkt"
proxy_model_state = torch.load(proxy_model_file)
# proxy_model = DNN(proxy_model_state['param'])
proxy_model = DNN(proxy_model_state['param'])
proxy_model.load_state_dict(proxy_model_state['model_dict'])
proxy_model.to(device)
dataset = CICIDS('Fuzzing')
dataloader = DataLoader(dataset, batch_size=512, shuffle=False)
output = "../data_Fuzzing_dnn.npy"
# getPredDataDL(dataloader, proxy_model, device, output)
target_model_file_svm = "/home/sunhanwu/work2021/TrafficAdversarial/experiment/src/modelFile/target_cicids_svm_Fuzzing.pkt"
target_model_file_lr = "/home/sunhanwu/work2021/TrafficAdversarial/experiment/src/modelFile/target_cicids_lr_Fuzzing.pkt"
target_model_file_if = "/home/sunhanwu/work2021/TrafficAdversarial/experiment/src/modelFile/target_cicids_if_Fuzzing.pkt"
target_model_file_dt = "/home/sunhanwu/work2021/TrafficAdversarial/experiment/src/modelFile/target_cicids_dt_Fuzzing.pkt"
param_lr = {
'C': 0.3
}
param_svm = {
'kernel': 'rbf',
}
param_if= {
'outliers_fraction1': 0.2,
"n_estimators": 200
}
# target_model_svm = TargetSVM(param_svm)
# target_model_svm.load(target_model_file_svm)
# svm_output = "/home/sunhanwu/work2021/TrafficAdversarial/experiment/src/data_Fuzzing_svm.npy"
# getPredDataML(dataloader, target_model_svm, svm_output)
#
# target_model_lr = TargetLR(param_lr)
# target_model_lr.load(target_model_file_lr)
# lr_output = "/home/sunhanwu/work2021/TrafficAdversarial/experiment/src/data_Fuzzing_lr.npy"
# getPredDataML(dataloader, target_model_lr, lr_output)
target_model_if = TargetIF(param_if)
target_model_if.load(target_model_file_if)
if_output = "/home/sunhanwu/work2021/TrafficAdversarial/experiment/src/data_Fuzzing_if.npy"
getPredDataML(dataloader, target_model_if, if_output)
# target_model_dt = TargetDT()
# target_model_dt.load(target_model_file_dt)
# dt_output = "/home/sunhanwu/work2021/TrafficAdversarial/experiment/src/data_Fuzzing_dt.npy"
# getPredDataML(dataloader, target_model_dt, dt_output)
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