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diy.py
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# -*- coding: UTF-8 -*-
import gc
import json
import sys
import os
import numpy
from matplotlib import pyplot as plt
sys.path.insert(0, '/usr/local/lib/python2.7/dist-packages/')
sys.path.insert(1, 'C:/Python27/Lib/site-packages')
import networkx as nx
def print_obj(obj):
"打印对象的所有属性"
print(obj.__dict__)
import pickle
def get_first_info(element):
G = element.old_g
# print_obj(G)
block_num = len(G._pred)
in_degrees = []
for i in range(block_num):
in_degrees.append([])
edge_num = 0
adj_matrix = []
for i in range(block_num):
adj_matrix.append([])
for j in range(block_num):
if i in G._pred[j]:
adj_matrix[i].append(j)
edge_num += 1
# for i in range(block_num):
# print(adj_matrix[i])
# print(edge_num)
# print(in_degrees)
return [block_num, edge_num, adj_matrix]
def get_original_filename(filename): # 去后缀
p = filename.split('\\')
return p[-1]
def get_info(f):
gc.collect()
testpath = f
print testpath
fr = open(testpath, 'r')
data1 = pickle.load(fr)
for element in data1.raw_graph_list:
current_list = {} # 一个函数的特征字典
current_list['src'] = get_original_filename(f) # 文件名
features = []
current_list['fname'] = element.funcname # 函数名
print element.funcname
G = element.g
current_list['n_num'] = len(G) # 节点数量
for i in range(len(G)):
temp = G.node[i]['v']
t = len(temp[0])
temp[0] = len(temp[1])
temp[1] = t
del temp[6]
for i in range(7):
temp[i] = round(float(temp[i]), 1)
features.append(temp)
current_list['features'] = features
current_list['succs'] = get_first_info(element)[2]
whole_list.append(current_list)
return whole_list
def write_file(out, list):
print out
if not os.path.isfile(out):
with open(out, 'w+') as json_file:
all_info = []
for i in list:
if i not in all_info:
all_info.append(i)
json.dump(all_info, json_file)
else:
all_info = []
with open(out, 'r') as json_file:
all_info = json.load(json_file)
with open(out, 'w') as json_file:
for i in list:
if i not in all_info:
all_info.append(i)
json.dump(all_info, json_file)
#sub_10F20 308 反编译代码有字符串，但是这个特征提取里没有字符串 constant，可能是间接引用的，不识别。看了下所有函数的特征，几乎都没有字符串常量，可能都是写在别的地方然后引用的。
#sub_166C4 393
if __name__ == '__main__':
whole_list = []
target_filename = sys.argv[2]
file = sys.argv[1]
if os.path.isfile(file):
info = get_info(file)
for i in info:
whole_list.append(i)
elif os.path.isdir(file):
for root, dir, files in os.walk(file):
for f in files:
if f.split('.')[-1] == 'ida': # 这是一个ida文件
info = get_info(root + "\\\\" + f)
for i in range(len(info)):
whole_list.append(info[i])
if len(whole_list) > 30:
write_file(target_filename, whole_list)
del whole_list
whole_list = []
write_file(target_filename, whole_list)
del whole_list
# no_repeated_list = []
#
# for w in whole_list:
# if w not in no_repeated_list:
# no_repeated_list.append(w)
#
# s = json.dumps(no_repeated_list)
# print s
# print len(whole_list)
# print(whole_list)
# print len(no_repeated_list)
#
# with open('.\\cfg.json', 'w') as f:
# f.write(s)
exit(0)
testpath = '.\\ida_files\\b_print_0.ida'
fr = open(testpath, 'r')
data1 = pickle.load(fr) #一个二进制文件的acfgs
#print(type(data1))
#print_obj(data1)
#print data1.raw_graph_list[393]
#print_obj(data1.raw_graph_list[393])
#nx.draw(data1.raw_graph_list[393].g,with_labels=True)
#plt.show()
print "一个二进制文件的所有函数的原始特征，list。"
print_obj(data1) #acfg list
print "\n"
print "一个函数的原始特征，由old_g（discovRe方法的ACFG），g（Genius方法的ACFG），fun_feature（表示函数级别的特征的向量）三部分构成"
for element in data1.raw_graph_list:
print('\nfunction name: ' + element.funcname + "\n")
# print_obj(element)
# print_obj(data1.raw_graph_list[393]) #一个函数的acfg
# feature=data1.raw_graph_list[393].fun_features
# feature = element.fun_features
# print feature
G = element.g
for i in range(len(G)):
print(G.node[i]['v'])
func_info = get_first_info(element)
# print_obj(element.old_g)
# print "函数级别特征： # 1 function calls # 2 logic instructions # 3 TransferIns # 4 LocalVariables # 5 BB basicblocks# 6 Edges # 7 IncommingCalls# 8 Intrs# 9 between # 10 strings # 11 consts"
print "\n"
# G=data1.raw_graph_list[393].old_g
# print G.node[0] # G.node[i]是dict
# for key, value in G.node[0].items():
# print('{key}:{value}'.format(key=key, value=value))
# 一个基本块的特征 #1'consts' 数字常量 #2'strings'字符串常量 #3'offs' offspring 字节点数量？ #4'numAs' 算数指令如INC #5'numCalls' 调用指令 #6'numIns' 指令数量 #7'numLIs' LogicInstructions 如AND #8'numTIs' 转移指令数量
# G=data1.raw_graph_list[0].g
# print "# 一个基本块的特征 #1'consts' 数字常量 #2'strings'字符串常量 #3'offs' offspring 字节点数量？ #4'numAs' 算数指令如INC #5'numCalls' 调用指令 #6'numIns' 指令数量 #7'numLIs' LogicInstructions 如AND #8'numTIs' 转移指令数量"
# print G.node[0]
# print "\n"
# for key, value in G.node[0].items():
# print('{key}:{value}'.format(key=key, value=value))
#oldg就是读取IDA的CFG，所以数量、方向等都一样；g根据old_g生成，也一样
#old g
# G = data1.raw_graph_list[0].old_g
# print_obj(G)
# block_num = len(G._pred)
# in_degrees = []
# for i in range(block_num):
# in_degrees.append(0)
# edge_num = 0
# adj_matrix = []
# for i in range(block_num):
# adj_matrix.append([])
# for j in range(block_num):
# if i in G._pred[j]:
# adj_matrix[i].append(1)
# in_degrees[j] += 1
# edge_num += 1
# else:
# adj_matrix[i].append(0)
# for i in range(block_num):
# print(adj_matrix[i])
# print(edge_num)
# print(in_degrees)
# nx.draw(G,with_labels=True)
# plt.title('old_g')
# plt.show()
# g
# G = data1.raw_graph_list[0].g
# nx.draw(G,with_labels=True)
# #plt.title('Genius_g')
# plt.show()
# # draw graph with labels
# pos = nx.spring_layout(G)
# nx.draw(G, pos)
# node_labels = nx.get_node_attributes(G, 'v') #networkx的node，由属性。g的属性为'v'，意为原始特征的vector。old_g的属性见cfg_constructor.py
# nx.draw_networkx_labels(G, pos, labels=node_labels)
# #plt.title('Genius_g with raw feature vector')
# plt.show()