backup

src/models/HierarchicalGraphModel.py

@@ -130,33 +130,34 @@ class HierarchicalGraphNeuralNetwork(nn.Module):
         local_batch.x = in_x
         return local_batch
 
-    # 基于多实例分解的CFG嵌入学习
-    def forward_MID_cfg_gnn(self, local_batch):
+    # 多实例分解的CFG嵌入学习
+    def forward_MID_cfg_gnn(self, local_batch: Batch):
+        device = torch.device('cuda')
         cfg_embeddings = []
-        # cfg_subgraph_loader是cfg(分解后的)列表，是二维的，每个元素都是一个acfg分解成的子图列表
         cfg_subgraph_loader = local_batch.cfg_subgraph_loader
-        # 聚合子图的嵌入，用以表示原本的cfg
         for acfg in cfg_subgraph_loader:
             subgraph_embeddings = []
-            # 遍历当前cfg的子图列表，每个元素都是一个子图，它是一个Data对象
-            # 计算子图的嵌入
             for subgraph in acfg:
                 in_x, edge_index = subgraph.x, subgraph.edge_index
+                batch = torch.zeros(in_x.size(0), dtype=torch.long, device=device)
                 for i in range(self.cfg_filter_length - 1):
                     out_x = getattr(self, 'CFG_gnn_{}'.format(i + 1))(x=in_x, edge_index=edge_index)
                     out_x = pt_f.relu(out_x, inplace=True)
                     out_x = self.dropout(out_x)
                     in_x = out_x
-                subgraph_embedding = torch.max(in_x, dim=0).values
-                subgraph_embeddings.append(subgraph_embedding)
+                subgraph_embedding = global_mean_pool(in_x, batch)
+                subgraph_embeddings.append(subgraph_embedding.squeeze(0))
             cfg_embedding = torch.stack(subgraph_embeddings).mean(dim=0)
             cfg_embeddings.append(cfg_embedding)
 
         cfg_embeddings = torch.stack(cfg_embeddings)
-        local_batch.x = cfg_embeddings
+        # 创建一个新的 batch 向量
+        batch_size = cfg_embeddings.size(0)
+        new_batch = torch.arange(batch_size)
+        local_batch.x = cfg_embeddings.to(device)
+        local_batch.batch = new_batch.to(device)
         return local_batch
 
-
     def aggregate_cfg_batch_pooling(self, local_batch: Batch):
         if self.pool == 'global_max_pool':
             x_pool = global_max_pool(x=local_batch.x, batch=local_batch.batch)

src/utils/PreProcessedDataset.py

@@ -69,8 +69,8 @@ def _simulating(_dataset, _batch_size: int):
 
 
 if __name__ == '__main__':
-    # root_path: str = '/root/autodl-tmp/'
-    root_path: str = 'D:\\hkn\\infected\\datasets\\proprecessed_pt'
+    root_path: str = '/root/autodl-tmp/'
+    # root_path: str = 'D:\\hkn\\infected\\datasets\\proprecessed_pt'
     i_batch_size = 2
     
     train_dataset = MalwareDetectionDataset(root=root_path, train_or_test='train')

src/utils/RealBatch.py

@@ -81,8 +81,6 @@ def multi_instance_decompose(acfg: Data):
     # g.add_edges_from(edge_index2edges(acfg.edge_index))
 
     return metis_MID(acfg)
-    # return structure_MID(acfg, g)
-    # return topological_MID(acfg, g)
 
 
 def metis_MID(acfg):

src/utils/util.py

@@ -0,0 +1,85 @@
+import os
+import shutil
+import random
+
+
+def transfer_remote():
+    samples_dir = '/root/autodl-tmp'
+    all_benign = '/root/autodl-tmp/all_benign'
+    one_family_malware = '/root/autodl-tmp/one_family_malware'
+
+    sample = ['malware', 'benign']
+    tags = ['test', 'train', 'valid']
+    for s in sample:
+        index = 0
+        for t in tags:
+            file_dir = os.path.join(samples_dir, '{}_{}'.format(t, s))
+            for file in os.listdir(file_dir):
+                dest_dir = all_benign if s == 'benign' else one_family_malware
+                shutil.copy(os.path.join(file_dir, file), os.path.join(dest_dir, str(index)))
+                index += 1
+
+    delete_all_remote()
+
+
+def delete_all_remote():
+    samples_dir = '/root/autodl-tmp'
+    sample = ['malware', 'benign']
+    tags = ['test', 'train', 'valid']
+    for s in sample:
+        for t in tags:
+            file_dir = os.path.join(samples_dir, '{}_{}'.format(t, s))
+            for f in os.listdir(file_dir):
+                os.remove(os.path.join(file_dir, f))
+
+
+# 重命名pt文件使之与代码相符
+def rename(file_dir, mal_or_be, postfix):
+    tag_set = ['train', 'test', 'valid']
+    for tag in tag_set:
+        data_dir = os.path.join(file_dir, '{}_{}{}/'.format(tag, mal_or_be, postfix))
+        for index, f in enumerate(os.listdir(data_dir)):
+            os.rename(os.path.join(data_dir, f), os.path.join(data_dir, 'm' + f))
+    for tag in tag_set:
+        data_dir = os.path.join(file_dir, '{}_{}{}/'.format(tag, mal_or_be, postfix))
+        for index, f in enumerate(os.listdir(data_dir)):
+            os.rename(os.path.join(data_dir, f), os.path.join(data_dir, '{}_{}.pt'.format(mal_or_be, index)))
+
+
+def split_samples(flag):
+    postfix = ''
+    file_dir = '/root/autodl-tmp'
+    if flag == 'one_family':
+        path = os.path.join(file_dir, 'one_family_malware')
+        tag = 'malware'
+    elif flag == 'standard':
+        path = os.path.join(file_dir, 'all')
+        postfix = '_backup'
+        tag = 'malware'
+    elif flag == 'benign':
+        path = os.path.join(file_dir, 'all_benign')
+        tag = 'benign'
+    else:
+        print('flag not implemented')
+        return
+
+    os_list = os.listdir(path)
+    random.shuffle(os_list)
+    # 8/1/1 分数据
+    train_len = int(len(os_list) * 0.6)
+    test_len = int(train_len / 3)
+    for index, f in enumerate(os_list):
+        if index < train_len:
+            shutil.copy(os.path.join(path, f), os.path.join(file_dir, 'train_{}'.format(tag) + postfix))
+        elif train_len <= index < train_len + test_len:
+            shutil.copy(os.path.join(path, f), os.path.join(file_dir, 'test_{}'.format(tag) + postfix))
+        else:
+            shutil.copy(os.path.join(path, f), os.path.join(file_dir, 'valid_{}'.format(tag) + postfix))
+    rename(file_dir, tag, postfix)
+
+
+if __name__ == '__main__':
+    # transfer_remote()
+    delete_all_remote()
+    split_samples('one_family')
+    split_samples('benign')