Genius 二进制文件函数特征提取的复现
0.原文
Scalable graph-based bug search for firmware images
https://github.com/qian-feng/Gencoding
1.Raw-feature-extractor 模块复现
1.1 生成 二进制文件的acfg。存储到.ida文件
The feature extraction is built on top of IDA-pro. We wrote the scripts based on ida-python and extract the attributed control flow graph. ``preprocessing_ida.py'' is the main program to extract the ACFG.
功能主要包括获取存储生成的ACFG路径、读取ida分析的文件等等。通过调用Get_func_cfgs_c这个函数获取二进制文件的ACFG集合，保存到 path/文件名.ida
# -*- coding: UTF-8 -*- # preprocessing_ida.py
import sys from func import * from raw_graphs import * from idc import * import os import argparse import raw_graphs def print_obj(obj): "打印对象的所有属性" print(obj.__dict__) def parse_command(): parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument("--path", type=str, help="The directory where to store the generated .ida file") args = parser.parse_args() return args if __name__ == '__main__': #print str(sys.argv) #['raw-feature-extractor/preprocessing_ida.py'] #print str(idc.ARGV) #['raw-feature-extractor/preprocessing_ida.py', '--path', 'C:\\Program1\\pycharmproject\\Genius3\\new'] #print idc.ARGV[2] #print type(idc.ARGV[2]) args = parse_command() #path = args.path path = idc.ARGV[2] analysis_flags = idc.GetShortPrm(idc.INF_START_AF) analysis_flags &= ~idc.AF_IMMOFF # turn off "automatically make offset" heuristic idc.SetShortPrm(idc.INF_START_AF, analysis_flags) idaapi.autoWait() cfgs = get_func_cfgs_c(FirstSeg()) binary_name = idc.GetInputFile() + '.ida' print path print binary_name fullpath = os.path.join(path, binary_name) pickle.dump(cfgs, open(fullpath,'w')) #print binary_name fr = open(fullpath,'r') data1 = pickle.load(fr) #print_obj(data1) print "end" #idc.Exit(0)
Get_func_cfgs_c定义在func.py。这里只截取一部分代码。
for funcea in Functions(SegStart(ea)): 这句代码功能是从起始位置，遍历所有的函数。（https://blog.csdn.net/weixin_39683163/article/details/110910148）
def get_func_cfgs_c(ea): # type: (object) -> object binary_name = idc.GetInputFile() raw_cfgs = raw_graphs(binary_name) externs_eas, ea_externs = processpltSegs() i = 0 for funcea in Functions(SegStart(ea)): funcname = get_unified_funcname(funcea) func = get_func(funcea) print i i += 1 icfg = cfg.getCfg(func, externs_eas, ea_externs) func_f = get_discoverRe_feature(func, icfg[0]) raw_g = raw_graph(funcname, icfg, func_f) #生成一个rawcfg。raw_graph是一个python class，定义在 raw_graph.py raw_cfgs.append(raw_g) # raw_graphs 是另一个python class，存储raw_graph的list。定义在 raw_graph.py print_obj(raw_g) #print(raw_g) 由于raw_graph、raw_graphs都是class，直接print只会打印<raw_graphs.raw_graphs instance at 0x09888FD0>，不能打印对象的属性。 #https://blog.51cto.com/steed/2046408 print_obj、 print(obj.__dict__) return raw_cfgs
通过添加打印信息，可知每一个函数的acfg的格式： <raw_graphs.raw_graph instance at 0x09958B48> 。即存在raw_graphs 这个class中的，raw_graph class instance。直接printinstance，看不到实例中的属性具体的值
raw_graph和raw_graphs的定义在raw_graph.py。这里只截取一部分。
class raw_graph: def __init__(self, funcname, g, func_f): #print "create" self.funcname = funcname self.old_g = g[0] self.g = nx.DiGraph() self.entry = g[1] self.fun_features = func_f self.attributing() class raw_graphs: #创建空的list，然后存储raw_graphs类的instance def __init__(self, binary_name): self.binary_name = binary_name self.raw_graph_list = [] def append(self, raw_g): self.raw_graph_list.append(raw_g) def __len__(self): return len(self.raw_graph_list)
1.2 读取保存的Ida文件内容
代码中，通过pickle.dump存储得到的raw_graphs，存储到.ida文件。首先尝试直接pickle.load读取内容
fr = open(fullpath,'rb') data1 = pickle.load(fr) print(data1)
报错
File "raw-feature-extractor/preprocessing_ida.py", line 40, in <module>
data1 = pickle.load(fr) #ImportError: No module named raw_graphs
ImportError: No module named raw_graphs
解决：
dump和load时应该采用同样的格式。如w对应r，wb对应rb。同时pickle存储时会把class的信息也存到文件中，所以load时需要再import 对应的class，或者在同一目录下load
pickle.load，直接print，格式为 <raw_graphs.raw_graph instance at 0x09958B48>，还是看不到实例中的属性具体的值
尝试直接肉眼读pickle存储的.ida文件，很难看懂。参考 https://docs.python.org/3/library/pickle.html https://www.cnblogs.com/tkqasn/p/6005025.html pickle和json的区别 json人类可读，而pickle不是人类可读的。
由于pickle无法直接肉眼读取，尝试打印raw_class 的instance的属性值。print无法打印具体的class instance的值，只能打印<raw_graphs.raw_graph instance at 0x09958B48> 这种格式的信息，没有办法读
这里参考 https://blog.51cto.com/steed/2046408，使用dict读取class instance的值
可以看到成功的打印了人类可以读懂的内容。包括acfg的具体值。
def print_obj(obj):
"打印对象的所有属性" print(obj.__dict__)
print_obj(raw_graphs)
{'raw_graph_list': [<raw_graphs.raw_graph instance at 0x097005A8>, <raw_graphs.raw_graph instance at 0x09872918>, <raw_graphs.raw_graph instance at 0x0A202760>, <raw_graphs.raw_graph instance at 0x0A202558>, <raw_graphs.raw_graph instance at 0x0A202B20>, <raw_graphs.raw_graph instance at 0x0A2023F0>, <raw_graphs.raw_graph instance at 0x0A2024E0>, <raw_graphs.raw_graph instance at 0x0A202AD0>, <raw_graphs.raw_graph instance at 0x0A127328>, <raw_graphs.raw_graph instance at 0x0A127D78>, <raw_graphs.raw_graph instance at 0x0A127968>,
<raw_graphs.raw_graph instance at 0x0A127300>, <raw_graphs.raw_graph instance at 0x0A127C38>, <raw_graphs.raw_graph instance at 0x0A127080>, <raw_graphs.raw_graph instance at 0x0A1AE260>, <raw_graphs.raw_graph instance at 0x0A1AEFD0>, <raw_graphs.raw_graph instance at 0x0A1AEDF0>, <raw_graphs.raw_graph instance at 0x0A1AE670>, <raw_graphs.raw_graph instance at 0x0A1AE0F8>, <raw_graphs.raw_graph instance at 0x0A1AE8F0>, <raw_graphs.raw_graph instance at 0x0A1AE2B0>, <raw_graphs.raw_graph instance at 0x0A251C10>, <raw_graphs.raw_graph instance at 0x0A251EB8>,
<raw_graphs.raw_graph instance at 0x0A251738>, <raw_graphs.raw_graph instance at 0x0A251850>, <raw_graphs.raw_graph instance at 0x0A2514E0>, <raw_graphs.raw_graph instance at 0x0A2519B8>, <raw_graphs.raw_graph instance at 0x0A251648>, <raw_graphs.raw_graph instance at 0x0A1424B8>, <raw_graphs.raw_graph instance at 0x0A142968>, <raw_graphs.raw_graph instance at 0x0A1425F8>, <raw_graphs.raw_graph instance at 0x0A142198>, <raw_graphs.raw_graph instance at 0x0A142300>, <raw_graphs.raw_graph instance at 0x0A142A08>, <raw_graphs.raw_graph instance at 0x0A11C328>,
<raw_graphs.raw_graph instance at 0x0A11CFD0>, <raw_graphs.raw_graph instance at 0x0A11C968>, <raw_graphs.raw_graph instance at 0x0A11C8A0>, <raw_graphs.raw_graph instance at 0x0A11C530>, <raw_graphs.raw_graph instance at 0x0A11C440>, <raw_graphs.raw_graph instance at 0x0A11CE18>, <raw_graphs.raw_graph instance at 0x0A126148>, <raw_graphs.raw_graph instance at 0x0A126BC0>, <raw_graphs.raw_graph instance at 0x0A126F80>, <raw_graphs.raw_graph instance at 0x0A126940>, <raw_graphs.raw_graph instance at 0x0A126080>, <raw_graphs.raw_graph instance at 0x0A1260D
print_obj(raw_g)
{'entry': 0, 'fun_features': [0, 0, 0, 0, 2, 1, 8, 3, 0.0, [], [42948, 102400]], 'old_g': <networkx.classes.digraph.DiGraph object at 0x085E8AD0>, 'g': <networkx.classes.digraph.DiGraph object at 0x0860DD70>, 'funcname': 'open'}
{'entry': 0, 'fun_features': [0, 0, 0, 0, 2, 1, 8, 3, 0.0, [], [42948, 102400]], 'old_g': <networkx.classes.digraph.DiGraph object at 0x085E8AD0>, 'g': <networkx.classes.digraph.DiGraph object at 0x0860DD70>, 'funcname': 'open'}
和raw_graph class定义一一对应： funcname、old_g、gentry、fun_feature（即论文Table one 定义的ACFG的属性）
class raw_graph:
def __init__(self, funcname, g, func_f):
#print "create"
self.funcname = funcname
self.old_g = g[0]
self.g = nx.DiGraph()
self.entry = g[1]
self.fun_features = func_f
self.attributing()
我们主要关心fun_features中的各个值的含义。通过discovRe.py中的函数实现。在ptycharm中按ctrl，可以跳转到对应的统计函数
def get_discoverRe_feature(func, icfg): start = func.startEA end = func.endEA features = [] FunctionCalls = getFuncCalls(func) #1 features.append(FunctionCalls) LogicInstr = getLogicInsts(func) #2 features.append(LogicInstr) Transfer = getTransferInsts(func) #3 features.append(Transfer) Locals = getLocalVariables(func) #4 features.append(Locals) BB = getBasicBlocks(func) #5 features.append(BB) Edges = len(icfg.edges()) #6 features.append(Edges) Incoming = getIncommingCalls(func) #7 features.append(Incoming) #8 Instrs = getIntrs(func) features.append(Instrs) between = retrieveGP(icfg) #9 features.append(between) strings, consts = getfunc_consts(func) features.append(strings) features.append(consts) return features
按顺序为
#1 function calls（本函数的函数调用指令（call jal jalr）数量）。。注意arm中没有这些指令
#2 logic instructions ，本函数的逻辑运算指令数量。如and、or的数量
#3 TransferIns 转移指令（如jmp arm中为mov）数量
#4 LocalVariables 局部变量数量
#5 BB basicblocks数量
#6 Edges icfg edges数量。icfg是另一篇论文dicovRe中的特征，这里暂时不管
#7 IncommingCalls，调用本函数的指令数量
#8 Intrs 指令数量
#9 between 结构特征中的betweeness。
nx.betweenness_centrality(G) 可以计算出每个node的betweeness
#10 strings 字符串
#11 consts 常量
问题是，这些特征是函数界别的。且betweeness应该是每个节点的betness，nx.betweenness_centrality(G) 返回值是list，为啥现在只显示是一个小数？
去看了源代码，在def betweeness(g):中计算确实是list，但是返回前做了计算转换为float，将基本块级别的betweeness转化为函数级别的betweeness。暂时就这样，以后有需求再改
def betweeness(g): #pdb.set_trace() betweenness = nx.betweenness_centrality(g) #print betweenness return betweenness
def retrieveGP(g):
bf = betweeness(g)
#close = closeness_centrality(g)
#bf_sim =
#close_sim =
x = sorted(bf.values())
value = sum(x)/len(x)
return round(value,5)
通过读源码发现，基本块级别的信息存在ACFG-g。
1.3 输出ACFG
1.3.1 概述
又看了代码。'old_g': <networkx.classes.digraph.DiGraph object at 0x085E8AD0>, 'g': <networkx.classes.digraph.DiGraph object at 0x0860DD70>
g是raw_graph中通过attributing()函数绘制的，也就是说其实论文中用到的特征并不是fun_features，而是 'g'。即g才是acfg，fun_features只是一个统计量，可以用于了解函数，但是没有其他用处
'g': <networkx.classes.digraph.DiGraph object at 0x0860DD70> G = nx.DiGraph() # 无多重边有向图
下一步工作是打印acfg，以及获取acfg中的信息。
需要明白，怎么访问python class 直接用 " . "
https://www.cnblogs.com/huangqihui/p/9355308.html
testpath="C:\Program1\pycharmproject\Genius3/acfgs/hpcenter.ida" fr = open(fullpath,'r') data1 = pickle.load(fr) print(type(data1)) #<type 'instance'> print(data1.raw_graph_list[0])
<type 'instance'>
<raw_graphs.raw_graph instance at 0x0902F7D8>
这样可以直接读取pickle dump保存的ida文件，不用每次都打开ida（（实际上暂时有些问题，先不管
print(data1.raw_graph_list[0].g) 这样打印不出来networkx的digraph。
先选择一个函数，选sub_166c4。
393
{'entry': 0, 'fun_features': [0, 0, 0, 0, 35, 53, 0, 183, 0.0601, [], [12, 1, 1, 0, 223, 76, 1, 48, 9, 0, 4294967295L, 4294967295L, 1, 1, 1, 1, 0, 0, 4294967294L, 0, 1, 0, 10, 3, 12]], 'old_g': <networkx.classes.digraph.DiGraph object at 0x08DA7AB0>, 'g': <networkx.classes.digraph.DiGraph object at 0x08DA7A30>, 'funcname': 'sub_166C4'}
testpath="C:\Program1\pycharmproject\Genius3/acfgs/hpcenter.ida" fr = open(fullpath,'r') data1 = pickle.load(fr) print(type(data1)) #<type 'instance'> print(data1.raw_graph_list[393].__dict__) print(data1.raw_graph_list[393].g) print(data1.raw_graph_list[393].g.nodes()) #print_obj(data1) #print cfgs.raw_graph_list[0] #idc.Exit(0) 输出： <type 'instance'> {'entry': 0, 'fun_features': [0, 0, 0, 0, 35, 53, 0, 183, 0.0601, [], [12, 1, 1, 0, 223, 76, 1, 48, 9, 0, 4294967295L, 4294967295L, 1, 1, 1, 1, 0, 0, 4294967294L, 0, 1, 0, 10, 3, 12]], 'old_g': <networkx.classes.digraph.DiGraph object at 0x0A01D730>, 'g': <networkx.classes.digraph.DiGraph object at 0x0A024E50>, 'funcname': 'sub_166C4'} [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]
ida中CFG
1.3.2 直接读取.ida文件
接下来要做到是怎样直接读pickle dump的.ida文件，不用每次都打开ida
写一个read_ida脚本，注意放在相同路径下（或通过其他方式导入相关module）
# -*- coding: UTF-8 -*-
import sys
def print_obj(obj):
"打印对象的所有属性"
print(obj.__dict__)
import pickle
if __name__ == '__main__':
testpath = "C:\Program1\pycharmproject\Genius3/acfgs/hpcenter.ida"
fr = open(testpath, 'r')
data1 = pickle.load(fr)
print(type(data1))
print_obj(data1)
print data1.raw_graph_list[0]
运行报错
File "C:\Program1\pycharmproject\Genius3\raw-feature-extractor\raw_graphs.py", line 5, in <module> import networkx as nx ImportError: No module named networkx
查看https://raw_graphs.py/ 。推测报错原因是，在有ida启动时，会自动选择系统的python2.7路径，导入该路径下的networkx包，而不经过ida，直接运行时没有导入，所以需要sys.path.insert
import sys #sys.path.insert(0, '/usr/local/lib/python2.7/dist-packages/') import networkx as nx
参考：
https://www.cnblogs.com/bigband/p/13745067.html
https://www.cnblogs.com/gdut-gordon/p/9336067.html
由于我这里换了windos运行，修改
#sys.path.insert(0, '/usr/local/lib/python2.7/dist-packages/') 增加 本地环境 sys.path.insert(1, 'C:/Python27/Lib/site-packages')
然后运行，就没问题了。可以直接读取之前pickle dump的ida文件
1.3.3 输出acfg信息
raw_graph.py中可以看出，acfg g实在discovRe的old_g的基础上构建的。
class raw_graph: def __init__(self, funcname, g, func_f): #print "create" self.funcname = funcname self.old_g = g[0] self.g = nx.DiGraph() self.entry = g[1] self.fun_features = func_f self.attributing() def __len__(self): return len(self.g) def attributing(self): self.obtainOffsprings(self.old_g) for node in self.old_g: fvector = self.retrieveVec(node, self.old_g) self.g.add_node(node) self.g.node[node]['v'] = fvector
可以看到g的节点的属性，有8个
def retrieveVec(self, id_, g): feature_vec = [] #numC0 numc = g.node[id_]['consts'] feature_vec.append(numc) #nums1 nums = g.node[id_]['strings'] feature_vec.append(nums) #offsprings2 offs = g.node[id_]['offs'] feature_vec.append(offs) #numAs3 numAs = g.node[id_]['numAs'] feature_vec.append(numAs) # of calls4 calls = g.node[id_]['numCalls'] feature_vec.append(calls) # of insts5 insts = g.node[id_]['numIns'] feature_vec.append(insts) # of LIs6 insts = g.node[id_]['numLIs'] feature_vec.append(insts) # of TIs7 insts = g.node[id_]['numTIs'] feature_vec.append(insts) return feature_vec
应该是一一对应的////或者old_G本身包含了一些信息，加上这些append的信息，构成上表
然而没找到betweeness。通过看源码发现betweeness在调用retrieveGP函数时实现，包含在 fun_feature内。
最后写下测试的脚本
# -*- coding: UTF-8 -*- import sys import sys 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 #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__': testpath = "C:\Program1\pycharmproject\Genius3/acfgs/hpcenter.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" print_obj(data1.raw_graph_list[393]) #一个函数的acfg print "\n" feature=data1.raw_graph_list[393].fun_features 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 feature 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[393].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[393].old_g nx.draw(G,with_labels=True) #plt.title('old_g') plt.show() # g G = data1.raw_graph_list[393].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()
输出：
一个二进制文件的所有函数的原始特征，list。 {'raw_graph_list': [<raw_graphs.raw_graph instance at 0x15DBA7B0>, <raw_graphs.raw_graph instance at 0x15DBA968>, <raw_graphs.raw_graph instance at 0x15DBA9E0>, <raw_graphs.raw_graph instance at 0x15DBAA80>, <raw_graphs.raw_graph instance at 0x15DBAAF8>, <raw_graphs.raw_graph instance at 0x15DBAB98>, <raw_graphs.raw_graph instance at 0x15DBAC38>, <raw_graphs.raw_graph instance at 0x15DBAE18>, <raw_graphs.raw_graph instance at 0x15DD10A8>, <raw_graphs.raw_graph instance at 0x15DD1350>, <raw_graphs.raw_graph instance at 0x15DD15D0>, <raw_graphs.raw_graph instance at 0x15DD1878>, <raw_graphs.raw_graph instance at 0x15DD1AD0>, <raw_graphs.raw_graph instance at 0x15DD1D28>, <raw_graphs.raw_graph instance at 0x15DD1FD0>, <raw_graphs.raw_graph instance at 0x15DDB288>, <raw_graphs.raw_graph instance at 0x15DDB4B8>, <raw_graphs.raw_graph instance at 0x15DDB710>, <raw_graphs.raw_graph instance at 0x15DDB968>, <raw_graphs.raw_graph instance at 0x15DDBBC0>, <raw_graphs.raw_graph instance at 0x15DDBDF0>, <raw_graphs.raw_graph instance at 0x15DE60A8>, <raw_graphs.raw_graph instance at 0x15DE6328>, <raw_graphs.raw_graph instance at 0x15DE6558>, <raw_graphs.raw_graph instance at 0x15DE67D8>, <raw_graphs.raw_graph instance at 0x15DE6A08>, <raw_graphs.raw_graph instance at 0x15DE6C88>, <raw_graphs.raw_graph instance at 0x15DE6EE0>, <raw_graphs.raw_graph instance at 0x15DF2170>, <raw_graphs.raw_graph instance at 0x15DF23A0>, <raw_graphs.raw_graph instance at 0x15DF25F8>, <raw_graphs.raw_graph instance at 0x15DF2850>, <raw_graphs.raw_graph instance at 0x15DF2A80>, <raw_graphs.raw_graph instance at 0x15DF2D00>, <raw_graphs.raw_graph instance at 0x15DF2F80>, <raw_graphs.raw_graph instance at 0x15DFD210>, <raw_graphs.raw_graph instance at 0x15DFD468>, <raw_graphs.raw_graph instance at 0x15DFD698>, <raw_graphs.raw_graph instance at 0x15DFD8F0>, <raw_graphs.raw_graph instance at 0x15DFDB70>, <raw_graphs.raw_graph instance at 0x15DFDDA0>, 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<raw_graphs.raw_graph instance at 0x16F45288>, <raw_graphs.raw_graph instance at 0x16F45468>, <raw_graphs.raw_graph instance at 0x16F45620>, <raw_graphs.raw_graph instance at 0x16F45828>, <raw_graphs.raw_graph instance at 0x16F459E0>, <raw_graphs.raw_graph instance at 0x16F45B98>, <raw_graphs.raw_graph instance at 0x16F45DA0>, <raw_graphs.raw_graph instance at 0x16F45F30>, <raw_graphs.raw_graph instance at 0x16F50148>, <raw_graphs.raw_graph instance at 0x16F50328>, <raw_graphs.raw_graph instance at 0x16F50508>], 'binary_name': 'hpcenter'} 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 {'entry': 0, 'fun_features': [0, 0, 0, 0, 35, 53, 0, 183, 0.0601, [], [12, 1, 1, 0, 223, 76, 1, 48, 9, 0, 4294967295L, 4294967295L, 1, 1, 1, 1, 0, 0, 4294967294L, 0, 1, 0, 10, 3, 12]], 'old_g': <networkx.classes.digraph.DiGraph object at 0x16CC7BD0>, 'g': <networkx.classes.digraph.DiGraph object at 0x16CD8F90>, 'funcname': 'sub_166C4'} 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 [0, 0, 0, 0, 35, 53, 0, 183, 0.0601, [], [12, 1, 1, 0, 223, 76, 1, 48, 9, 0, 4294967295L, 4294967295L, 1, 1, 1, 1, 0, 0, 4294967294L, 0, 1, 0, 10, 3, 12]] # 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 {'v': [[1], [], 10, 0, 0, 2, 0, 0]}
