17c1ac88b1
Complete the reproduction of the Raw-feature-extractor: The purpose of read_idaFILE.py is to read the raw-feature from the generated .ida file and display
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
# -*- coding: UTF-8 -*-
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import sys
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import sys
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from matplotlib import pyplot as plt
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sys.path.insert(0, '/usr/local/lib/python2.7/dist-packages/')
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sys.path.insert(1, 'C:/Python27/Lib/site-packages')
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import networkx as nx
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def print_obj(obj):
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"打印对象的所有属性"
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print(obj.__dict__)
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import pickle
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#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。
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#sub_166C4 393
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if __name__ == '__main__':
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testpath = "C:\Program1\pycharmproject\Genius3/acfgs/hpcenter.ida"
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fr = open(testpath, 'r')
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data1 = pickle.load(fr) #一个二进制文件的acfgs
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#print(type(data1))
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#print_obj(data1)
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#print data1.raw_graph_list[393]
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#print_obj(data1.raw_graph_list[393])
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#nx.draw(data1.raw_graph_list[393].g,with_labels=True)
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#plt.show()
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print "一个二进制文件的所有函数的原始特征，list。"
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print_obj(data1) #acfg list
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print "\n"
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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"
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print_obj(data1.raw_graph_list[393]) #一个函数的acfg
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print "\n"
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feature=data1.raw_graph_list[393].fun_features
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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"
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print feature
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print "\n"
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# G=data1.raw_graph_list[393].old_g
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# print G.node[0] # G.node[i]是dict
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# for key, value in G.node[0].items():
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# print('{key}:{value}'.format(key=key, value=value))
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# 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
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G=data1.raw_graph_list[393].g
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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"
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print G.node[0]
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print "\n"
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# for key, value in G.node[0].items():
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# print('{key}:{value}'.format(key=key, value=value))
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#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
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#old g
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G = data1.raw_graph_list[393].old_g
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nx.draw(G,with_labels=True)
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#plt.title('old_g')
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plt.show()
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# g
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G = data1.raw_graph_list[393].g
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nx.draw(G,with_labels=True)
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#plt.title('Genius_g')
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plt.show()
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# draw graph with labels
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pos = nx.spring_layout(G)
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nx.draw(G, pos)
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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
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nx.draw_networkx_labels(G, pos, labels=node_labels)
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#plt.title('Genius_g with raw feature vector')
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plt.show()
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# 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
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# 2 logic instructions ，本函数的逻辑运算指令数量。如and、or的数量
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# 3 TransferIns 转移指令（如jmp arm中为mov）数量
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# 4 LocalVariables 局部变量数量
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# 5 BB basicblocks数量
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# 6 Edges icfg edges数量。icfg是另一篇论文dicovRe中的特征，这里暂时不管
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# 7 IncommingCalls，调用本函数的指令数量
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# 8 Intrs 指令数量
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# 9 between 结构特征中的betweeness。
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# 10 strings 字符串
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# 11 consts 数字常量 |