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])

<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。
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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]}

posted @ 2021-11-19 16:24 Erio 阅读(1777) 评论(2) 编辑收藏举报

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