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evl.py
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evl.py
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import cPickle as pickle
import sys
import os
import pdb
import hungarian
import numpy
import matplotlib.pylab as plt
from db import *
def accuracy1(ddir):
FP = {}
hit = []
miss = []
total = 0
datas = {}
false = {}
for exeName in os.listdir(ddir):
if '.match' in exeName:
print exeName
data = pickle.load(open(os.path.join(ddir,exeName),'r'))
datas[exeName] = data
total += len(data)
for funname in data:
allnames = [v[0] for v in data[funname] if funname == v[0]]
if len(allnames) == 0:
false[funname] = 1
continue
m=min([v[1][0] for v in data[funname]])
re=[v[0] for v in data[funname] if v[1][0] == m]
if funname in re:
hit.append(funname)
else:
miss.append((exeName,funname))
if len(set(re)) != 1:
FP[funname] = (exeName,[v for v in re if funname != v])
fp1= {}
for funname in FP:
re=FP[funname][1]
x=[v for v in re if v not in false]
fp1[funname] = (FP[funname][0], len(x))
pdb.set_trace()
fp = sum([fp1[v][1] for v in fp1])
print len(hit)
print len(miss)
print (len(hit)-len(miss))*1.0 / total
print fp
def convert_llvmtostrip(strip_name, llvm_indexes):
llvm_to_strip = {}
src_name, src_version = split(strip_name)
strip_indexes = obtain_stripindexes(strip_name, src_version)
strip_bases = obtain_stripbases(src_name, src_version)
for name in llvm_indexes:
base = obtain_base(name, strip_indexes)
if base in strip_bases:
llvm_to_strip[name] = strip_bases[base]
return llvm_to_strip
def obtain_groundtruth(src_bname, dst_bname):
src_bname, blevel, bsymbol, barch = parse_binary_name(src_name)
dst_bname, dlevel, dsymbol, darch = parse_binary_name(dst_name)
conn, cur = connect()
def accuracy(scorelist, src_name, dst_name, src_indexes, dst_indexes):
matrix = []
#src_indexes = scorelist.keys()
#dst_indexes = scorelist[scorelist.keys()[0]].keys()
gt, src_strip_indexes, dst_strip_indexes = obtain_node_indexes(src_name,dst_name,src_indexes,dst_indexes)
src_len = len(src_indexes)
dst_len = len(dst_indexes)
matrix_len = max(src_len, dst_len)
for row_id in xrange(matrix_len):
row = []
if row_id >= src_len:
src_name = 'src_dummy'
else:
src_name = src_indexes[row_id]
for column_id in xrange(matrix_len):
if column_id >= dst_len:
dst_name = 'dst_dummy'
else:
dst_name = dst_indexes[column_id]
if row_id == 15 and column_id == 9:
pdb.set_trace()
print "s"
try:
if src_name == 'src_dummy' or dst_name == 'dst_dummy':
cost = 1000
else:
cost = scorelist[src_name][dst_name]
except:
cost = scorelist[dst_name][src_name]
row.append(cost)
matrix.append(row)
individual_evl(matrix, src_indexes, dst_indexes, gt, src_strip_indexes, dst_strip_indexes)
BGM_evl(matrix, src_indexes, dst_indexes, gt, src_strip_indexes, dst_strip_indexes)
def individual_evl(matrix, src_indexes, dst_indexes, gt, src_strip_indexes, dst_strip_indexes):
total = len(gt)
mid = 0
hit = 0
fp = 0
for row in matrix:
min_score = min(row)
for column_id in xrange(len(row)):
cost = row[column_id]
if cost == min_score:
if mid < len(src_indexes):
src_name = src_indexes[mid]
else:
src_name = 'src_dummy'
if column_id < len(dst_indexes):
dst_name = dst_indexes[column_id]
else:
dst_name = 'dst_dummy'
src = obtain_base(src_name, src_strip_indexes)
dst = obtain_base(dst_name, dst_strip_indexes)
if (src, dst) in gt:
hit += 1
else:
fp += 1
mid += 1
print "Individual matching false positives = " + str((fp) * 1.0/(fp+hit))
print "Individual matching accuracy rate =" + str(hit*1.0/total)
def BGM_evl(matrix, src_indexes, dst_indexes, gt, src_strip_indexes, dst_strip_indexes):
mapping = hungarian.lap(matrix)
distance = caldistance(mapping, matrix)
hit = 0
print "Binary Similarity Socre is = " + str(distance)
pdb.set_trace()
index = -1
miss = []
for i in mapping[0]:
index += 1
try:
src = obtain_base(src_indexes[index], src_strip_indexes)
dst = obtain_base(dst_indexes[i], dst_strip_indexes)
except:
continue
if (src, dst) in gt:
hit += 1
else:
#pdb.set_trace()
miss.append((src,dst))
print "s"
re = st(gt, miss)
pdb.set_trace()
print "The BGM matching recall rate =" + str(hit*1.0/len(gt))
def st(gt, miss):
t = []
for src, dst in miss:
for x, y in gt:
if src == x:
t.append((hex(src), (hex(src), hex(dst)), (hex(x),hex(y))))
return t
def obtain_base(name, indexes):
if 'sub_' in name:
name = name.split('sub_')[1]
base = int(name, 16)
return base
else:
if 'driver_' in name:
name = name.split('driver_')[1]
if name == 'start':
name = '_start'
if name in indexes:
return indexes[name]
base = int(name, 16)
return base
if name in indexes:
return indexes[name]
return False
def caldistance(mapping, node_matrix):
cost = 0
for i in xrange(len(mapping[0])):
cost += node_matrix[i][mapping[0][i]]
return cost
if __name__=="__main__":
ddir = sys.argv[-1]
accuracy(ddir)