From aa0b4c181701493042187478ae4346f54ba4e210 Mon Sep 17 00:00:00 2001
From: yifei cheng <chengyifei@iie.ac.cn>
Date: Mon, 26 Jun 2023 12:35:06 +0000
Subject: Upload New File

---
 attack/attackTargetModel.py | 82 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 82 insertions(+)
 create mode 100644 attack/attackTargetModel.py

diff --git a/attack/attackTargetModel.py b/attack/attackTargetModel.py
new file mode 100644
index 0000000..114ea44
--- /dev/null
+++ b/attack/attackTargetModel.py
@@ -0,0 +1,82 @@
+"""
+Date: 2022-04-12
+Author: sunhanwu@iie.ac.cn
+Desc: attack the target model use adversarial examples
+"""
+from tqdm import tqdm
+import numpy as np
+import torch
+from sklearn.metrics import accuracy_score, recall_score, f1_score, confusion_matrix
+from attack.adversarialDataset import AdversarialC2Data
+from TargetModel.TargetSVM import TargetSVM
+from TargetModel.TargetLR import TargetLR
+from TargetModel.TargetDT import TargetDT
+from TargetModel.TargetRF import TargetRF
+from TargetModel.TargetKNN import TargetKNN
+from TargetModel.TargetLSTM import TargetLSTM
+from torch.utils.data import DataLoader
+
+def attackDLModel(model, dataloader, device, batch_size=128):
+    """
+
+    :param model:
+    :param data:
+    :return:
+    """
+    model.to(device)
+    model.eval()
+    y_true = []
+    y_pred = []
+    for batch_x, batch_y  in tqdm(dataloader):
+        batch_x = batch_x.to(device).float()
+        batch_y = batch_y.to(device)
+        output = model(batch_x)
+        y_true += batch_y.detach().cpu().data.numpy().reshape(-1).tolist()
+        y_pred += torch.argmax(output, dim=1).detach().cpu().data.numpy().tolist()
+    acc = accuracy_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred, average='macro')
+    f1 = f1_score(y_true, y_pred, average='macro')
+    con_matrix = confusion_matrix(y_true, y_pred)
+    print("acc: {:.2%}, recall: {:.2%}, f1: {:.2%}".format(acc, recall, f1))
+    print(con_matrix)
+
+def attackMLModel(model, dataloader):
+    """
+
+    :param model:
+    :param dataloader:
+    :return:
+    """
+    y_pred, y_true = model.eval(dataloader)
+    acc = accuracy_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred, average='macro')
+    f1 = f1_score(y_true, y_pred, average='macro')
+    con_matrix = confusion_matrix(y_true, y_pred)
+    print("acc: {:.2%}, recall: {:.2%}, f1: {:.2%}".format(acc, recall, f1))
+    print("con_metric:\n {}".format(con_matrix))
+
+if __name__ == '__main__':
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    targetArch = "lstm"
+    surrogateArch = "lstm"
+    botname = "Gozi"
+    theta = 0.01
+    gamma = 0.1
+    times = 20
+    target_class=0
+    targetModelFile = "../modelFile/target_mta_cicflowmeter_{}_{}.pkt".format(targetArch, botname)
+    adverDataFile = "../adversarialData/advdata_mta_cicflowmeter_{}_{}_{}_{}_{}.npy".format(surrogateArch, botname, theta, gamma, times)
+    advdata = AdversarialC2Data(adverDataFile, target_class)
+    advdataloader = DataLoader(advdata, batch_size=128, shuffle=True, drop_last=False)
+
+    print("Load adv model: {}".format(targetModelFile))
+    adv_state = torch.load(targetModelFile)
+    print("model param: {}".format(adv_state['param']))
+    targetModel = TargetLSTM(adv_state['param'])
+    targetModel.load_state_dict(adv_state['model_dict'])
+    targetModel.to(device)
+    attackDLModel(targetModel, advdataloader, device, batch_size=128)
+
+
+
+
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