多线程完善 特征整合

bert/obtain_inst_vec.py

@@ -14,6 +14,10 @@ model_file = os.path.join("./bert/pytorch_model.bin")
 tokenizer_file = os.path.join("./bert/tokenizer-inst.all.json")
 config_file = os.path.join('./bert/bert.json')
 
+
+# 禁用分词器多线程
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
 # from my_data_collator import MyDataCollatorForPreTraining
 # model_file = os.path.join("./pytorch_model.bin")
 # tokenizer_file = os.path.join("./tokenizer-inst.all.json")
@@ -23,7 +27,7 @@ config_file = os.path.join('./bert/bert.json')
 def load_model():
     config = BertConfig.from_json_file(config_file)
     model = BertForPreTraining(config)
-    state_dict = torch.load(model_file)
+    state_dict = torch.load(model_file, map_location='cpu')
     model.load_state_dict(state_dict)
     model.eval()
 
@@ -101,7 +105,7 @@ def generate_inst_vec(inst, method="mean"):
 
 def bb2vec(inst):
     tmp = generate_inst_vec(inst, method="mean")
-    return list(np.mean(tmp.detach().numpy(), axis=0))
+    return list(np.mean(tmp.detach().numpy(), axis=0).astype(float))
 
 
 if __name__ == "__main__":

exe2json.py

@@ -11,11 +11,10 @@ import os
 import concurrent.futures
 import multiprocessing
 
-# 禁用分词器多线程
+
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
 ret_trap_opcode_family = ["ret", "hlt", "int3", "ud2"]
 
-sample_type = 'benign'
+sample_type = 'malware'
 
 
 def extract_opcode(disasm_text):
@@ -144,7 +143,7 @@ def get_graph_cfg_r2pipe(r2pipe_open, file_path, output_path, file_name):
                 # block_feature_list = []
 
                 # 暂时将bb地址作为特征  后续将操作码集中转化为特征
-                block_feature_list = block_addr
+                block_feature_list.append(block_addr)
                 acfg_feature_item.append({'addr': block_addr, 'opcode': block_Statement})
 
             # 过滤不存在的边
@@ -214,9 +213,9 @@ def exe_to_json(file_path):
     output_path = f"./out/json/{sample_type}"
     file_fingerprint = calc_sha256(file_path)
     if os.path.exists(os.path.join(output_path, file_fingerprint + '.jsonl')):
-        logger.info(f"二进制可执行文件已解析，文件名{file_path}")
+        # logger.info(f"二进制可执行文件已解析，文件名{file_path}")
         return
-    logger.info(f"开始解析，文件名{file_path}")
+    # logger.info(f"开始解析，文件名{file_path}")
 
     # 获取r2pipe并解析文件  解析完即释放r2
     r2 = r2pipe.open(file_path, flags=['-2'])
@@ -248,7 +247,7 @@ def exe_to_json(file_path):
     # json写入
     os.makedirs(output_path, exist_ok=True)
     save_json(os.path.join(output_path, file_fingerprint + '.jsonl'), json_obj)
-    logger.info(f"解析完成，文件名{file_path}")
+    # logger.info(f"解析完成，文件名{file_path}")
     return
 
 
@@ -258,7 +257,7 @@ if __name__ == '__main__':
     sample_file_list = os.listdir(sample_file_path)
     print(f"max worker {os.cpu_count()}")
     with multiprocessing.Pool(processes=os.cpu_count()) as pool:
-        result = list(tqdm(pool.imap(exe_to_json, [os.path.join(sample_file_path, file_name) for file_name in sample_file_list]), total=len(sample_file_list)))
+        result = list(tqdm(pool.imap_unordered(exe_to_json, [os.path.join(sample_file_path, file_name) for file_name in sample_file_list]), total=len(sample_file_list)))
 
 
     # with concurrent.futures.ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:

json_feature2json.py

@@ -0,0 +1,50 @@
+import os
+from my_utils import save_json, load_json, setup_logger
+from bert.obtain_inst_vec import bb2vec
+import multiprocessing
+from tqdm import tqdm
+import warnings
+
+# 忽略输出torch cpu执行警告
+warnings.filterwarnings('ignore', category=UserWarning)
+
+
+def addr2vec(base_file_path):
+    # 从路径拆分文件名与路径
+    file_name = str(os.path.basename(base_file_path))
+    file_path = str(os.path.dirname(base_file_path))
+
+    # 忽略已生成的文件
+    if os.path.exists(os.path.join(file_path, 'final', file_name)):
+        return
+
+    # 如果不是路径则开始转化
+    if file_name:
+        file_json = load_json(base_file_path)
+        # 确保存在基础文件而不存在特征文件的情况
+        feature_json = load_json(os.path.join(file_path, 'feature', file_name)) if os.path.exists(
+            os.path.join(file_path, 'feature', file_name)) else None
+        if feature_json is not None:
+            feature_set = {}
+            for item in feature_json:
+                feature_set[item['addr']] = bb2vec(item['opcode'])
+            for item in file_json['acfg_list']:
+                bb_feature_addr_list = item['block_features']
+                item['block_features'] = [feature_set[key] for key in bb_feature_addr_list]
+            save_json(os.path.join(file_path, 'final', file_name), file_json)
+        else:
+            logger.error(f'文件{file_name}不存在特征文件')
+    return
+
+
+if __name__ == '__main__':
+    logger = setup_logger('feature2json', './log/feature2json.log')
+    sample_type = 'benign'
+    json_path = os.path.join(f'./out/json/{sample_type}')
+    json_files = os.listdir(json_path)
+    with multiprocessing.Pool(processes=os.cpu_count()) as pool:
+        result = list(tqdm(pool.imap_unordered(addr2vec, [os.path.join(json_path, file) for file in json_files]),
+                           total=len(json_files)))
+
+    # for json_file in json_files:
+    #     addr2vec(json_path, json_file)

my_utils.py

@@ -1,6 +1,6 @@
 import logging
 import os
-
+import json
 
 """
 日志工具
@@ -45,7 +45,7 @@ THREAD_FULL = os.cpu_count()
 THREAD_HALF = int(os.cpu_count() / 2)
 
 
-def multi_thread(func, args, thread_num=THREAD_FULL):
+def multi_thread_order(func, args, thread_num=THREAD_FULL):
     """
     多线程执行函数
     :param func: 函数
@@ -73,10 +73,22 @@ def multi_thread(func, args, thread_num=THREAD_FULL):
     return result
 
 
-def save_json(filename, data):
+def multi_thread_disorder(func,  thread_num=THREAD_FULL, **args):
-    import json
+    import multiprocessing
+    from tqdm import tqdm
+    with multiprocessing.Pool(processes=thread_num) as pool:
+        list(tqdm(pool.imap_unordered(func, args), total=len(args)))
 
+
+def save_json(filename, data):
     data = json.dumps(data)
     file = open(filename, 'w')
     file.write(data)
     file.close()
+
+
+def load_json(filename):
+    file = open(filename, 'r')
+    data = json.loads(file.read())
+    file.close()
+    return data