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6_gen_search_tfrecord_binseeker.py
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6_gen_search_tfrecord_binseeker.py
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#!/usr/bin/python
# -*- coding: UTF-8 -*-
# 将所有的.o文件利用IDA 进行反汇编
import config
import os
import subprocess
import glob
import csv
import tensorflow as tf
import numpy as np
import networkx as nx
import itertools
import config
import shutil
import re
# @numba.jit
def construct_learning_dataset(uid_pair_list):
""" Construct pairs dataset to train the model.
attributes:
adj_matrix_all store each pairs functions' graph info, （i,j)=1 present i--》j, others （i,j)=0
features_all store each pairs functions' feature map
"""
# print " start generate adj matrix pairs..."
cfgs_1, cfgs_2, dfgs_1, dfgs_2 = generate_graph_pairs(uid_pair_list)
# print " start generate features pairs..."
### !!! record the max number of a function's block
feas_1, feas_2, max_size, num1, num2 = generate_features_pair(uid_pair_list)
return cfgs_1, cfgs_2, dfgs_1, dfgs_2, feas_1, feas_2, num1, num2, max_size
# @numba.jit
def generate_graph_pairs(uid_pair_list):
""" construct all the function pairs' cfg matrix.
"""
cfgs_1 = []
cfgs_2 = []
dfgs_1 = []
dfgs_2 = []
# traversal all the pairs
count = 0
for uid_pair in uid_pair_list:
print uid_pair
# adj_matrix_pair = []
# each pair process two function
graph_cfg = nx.read_adjlist(os.path.join(config.CVE_FEATURE_DIR, uid_pair[0]+"_cfg.txt"))
# graph = uid_graph[uid_pair[0]]
# origion_adj_1 = np.array(nx.convert_matrix.to_numpy_matrix(graph, dtype=float))
# origion_adj_1.resize(size, size, refcheck=False)
# adj_matrix_all_1.append(origion_adj_1.tolist())
adj_arr = np.array(nx.convert_matrix.to_numpy_matrix(graph_cfg, dtype=float))
adj_str = adj_arr.astype(np.string_)
cfgs_1.append(",".join(list(itertools.chain.from_iterable(adj_str))))
graph_dfg = nx.read_adjlist(os.path.join(config.CVE_FEATURE_DIR, uid_pair[0]+"_dfg.txt"))
adj_dfg_arr = np.array(nx.convert_matrix.to_numpy_matrix(graph_dfg, dtype=float))
adj_dfg_str = adj_dfg_arr.astype(np.string_)
dfgs_1.append(",".join(list(itertools.chain.from_iterable(adj_dfg_str))))
graph_cfg = nx.read_adjlist(os.path.join(config.FEA_DIR, uid_pair[1]+"_cfg.txt"))
# graph = uid_graph[uid_pair[1]]
# origion_adj_2 = np.array(nx.convert_matrix.to_numpy_matrix(graph, dtype=float))
# origion_adj_2.resize(size, size, refcheck=False)
# adj_matrix_all_2.append(origion_adj_2.tolist())
adj_arr = np.array(nx.convert_matrix.to_numpy_matrix(graph_cfg, dtype=float))
adj_str = adj_arr.astype(np.string_)
cfgs_2.append(",".join(list(itertools.chain.from_iterable(adj_str))))
graph_dfg = nx.read_adjlist(os.path.join(config.FEA_DIR, uid_pair[1]+"_dfg.txt"))
adj_dfg_arr = np.array(nx.convert_matrix.to_numpy_matrix(graph_dfg, dtype=float))
adj_dfg_str = adj_dfg_arr.astype(np.string_)
dfgs_2.append(",".join(list(itertools.chain.from_iterable(adj_dfg_str))))
# cur_cve + os.sep + cur_cve_program + os.sep + config.STEP6_CVE_FUN_LIST.get(cur_cve),
return cfgs_1, cfgs_2, dfgs_1, dfgs_2
# @numba.jit
def generate_features_pair(uid_pair_list):
""" Construct each function pairs' block feature map.
"""
feas_1 = []
feas_2 = []
num1 = []
num2 = []
node_length = []
# traversal all the pairs
count = 0
for uid_pair in uid_pair_list:
print uid_pair
node_vector = []
block_feature_dic={}
with open(os.path.join(config.CVE_FEATURE_DIR, uid_pair[0]+"_fea.csv"), "r") as fp:
for line in csv.reader(fp):
if line[0] == "":
continue
# read every bolck's features
block_feature = [float(x) for x in (line[1:16])]
# print line[0],block_feature
# 删除某一列特征
# del block_feature[6]
block_feature_dic.setdefault(str(line[0]), block_feature)
graph_cfg = nx.read_adjlist(os.path.join(config.CVE_FEATURE_DIR, uid_pair[0] + "_cfg.txt"))
for node in graph_cfg.nodes():
node_vector.append(block_feature_dic[node])
node_length.append(len(node_vector))
num1.append(len(node_vector))
node_arr = np.array(node_vector)
node_str = node_arr.astype(np.string_)
feas_1.append(",".join(list(itertools.chain.from_iterable(node_str))))
node_vector = []
block_feature_dic={}
with open(os.path.join(config.FEA_DIR, uid_pair[1]+"_fea.csv"), "r") as fp:
for line in csv.reader(fp):
if line[0] == "":
continue
# read every bolck's features
block_feature = [float(x) for x in (line[1:16])]
# 删除某一列特征
# del block_feature[6]
block_feature_dic.setdefault(str(line[0]), block_feature)
graph_cfg = nx.read_adjlist(os.path.join(config.FEA_DIR, uid_pair[1] + "_cfg.txt"))
for node in graph_cfg.nodes():
node_vector.append(block_feature_dic[node])
node_length.append(len(node_vector))
num2.append(len(node_vector))
node_arr = np.array(node_vector)
node_str = node_arr.astype(np.string_)
feas_2.append(",".join(list(itertools.chain.from_iterable(node_str))))
num1_re = np.array(num1)
num2_re = np.array(num2)
#num1_re = num1_arr.astype(np.string_)
#num2_re = num2_arr.astype(np.string_)
return feas_1, feas_2, np.max(node_length),num1_re,num2_re
cve_list = {}
cve_filters = glob.glob(config.CVE_FEATURE_DIR + os.sep + "*")
print "cve_filters:",cve_filters
for cur_cve_dir in cve_filters:
if os.path.isdir(cur_cve_dir):
cve_list[cur_cve_dir.split(os.sep)[-1]] = []
cve_program_filters = glob.glob(cur_cve_dir + os.sep + "*")
print "cve_program_filters:",cve_program_filters
for cur_cve_program in cve_program_filters:
if os.path.isdir(cur_cve_program):
cve_list[cur_cve_dir.split(os.sep)[-1]].append(cur_cve_program.split(os.sep)[-1])
search_program_function_list = {}
for program in config.STEP6_SEARCH_PROGRAM_ARR:
search_program_function_list[program] = {}
tempdir = config.FEA_DIR + os.sep + str(program)
filters = glob.glob(config.FEA_DIR + os.sep + str(program) + os.sep + "*")
for i in filters:
if os.path.isdir(i):
search_program_function_list[program][i.split(os.sep)[-1]] = []
search_list = i + os.sep + "functions_list.csv"
with open(search_list, "r") as fp:
for line in csv.reader(fp):
# print line
if line[0] == "":
continue
search_program_function_list[program][i.split(os.sep)[-1]].append([line[0],line[6]])
# print line[0]
# 清空文件夹
# shutil.rmtree(config.SEARCH_VULSEEKER_TFRECORD_DIR)
# os.mkdir(config.SEARCH_VULSEEKER_TFRECORD_DIR)
# 一个CVE 一个 tfrecord， 以CVE命名
print "cve_list:",cve_list
for cur_cve in cve_list.keys():
if not os.path.exists(config.SEARCH_VULSEEKER_TFRECORD_DIR + os.sep + cur_cve):
os.mkdir(config.SEARCH_VULSEEKER_TFRECORD_DIR + os.sep + cur_cve)
for program,version_dict in search_program_function_list.items():
print "program",program
for version in version_dict.keys():
print " version",version
search_pair_list = []
label_list = []
for functions in version_dict.get(version):
function_name = functions[0]
bin_path_arr = re.split(r"[/,//,\,\\]",functions[1])
bin_path = bin_path_arr[-3] + os.sep + bin_path_arr[-2] + os.sep + bin_path_arr[-1]
for cur_cve_program in cve_list.get(cur_cve):
# print functions
search_pair_list.append(
[cur_cve+os.sep+cur_cve_program+os.sep+config.STEP6_CVE_FUN_LIST.get(cur_cve),
program+os.sep+version+os.sep+function_name])
label_list.append(function_name+"###"+bin_path)
search_cfg_1, search_cfg_2, search_dfg_1, search_dfg_2, search_fea_1, search_fea_2, search_num1, search_num2, search_max \
= construct_learning_dataset(search_pair_list)
node_list = np.linspace(search_max,search_max, len(search_pair_list),dtype=int)
cur_path = config.SEARCH_VULSEEKER_TFRECORD_DIR + os.sep + cur_cve
tf_file_name = version+"__NUM__"+str(len(search_pair_list))+ "#" + str(len(cve_list.get(cur_cve)))
writer = tf.python_io.TFRecordWriter(cur_path + os.sep + tf_file_name +".tfrecord")
for item1,item2,item3,item4,item5,item6, item7,item8, item9, item10 in itertools.izip(
search_cfg_1, search_cfg_2, search_dfg_1, search_dfg_2, search_fea_1, search_fea_2,
search_num1, search_num2, node_list, label_list):
example = tf.train.Example(
features = tf.train.Features(
feature = {
'cfg_1': tf.train.Feature(bytes_list=tf.train.BytesList(value=[item1])),
'cfg_2': tf.train.Feature(bytes_list=tf.train.BytesList(value=[item2])),
'dfg_1': tf.train.Feature(bytes_list=tf.train.BytesList(value=[item3])),
'dfg_2': tf.train.Feature(bytes_list=tf.train.BytesList(value=[item4])),
'fea_1': tf.train.Feature(bytes_list=tf.train.BytesList(value=[item5])),
'fea_2': tf.train.Feature(bytes_list=tf.train.BytesList(value=[item6])),
'num1':tf.train.Feature(int64_list = tf.train.Int64List(value=[item7])),
'num2':tf.train.Feature(int64_list = tf.train.Int64List(value=[item8])),
'max': tf.train.Feature(int64_list=tf.train.Int64List(value=[item9])),
'label': tf.train.Feature(bytes_list=tf.train.BytesList(value=[item10]))}))
serialized = example.SerializeToString()
writer.write(serialized)
writer.close()