-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathfingerprint_test.py
More file actions
169 lines (165 loc) · 7.03 KB
/
Copy pathfingerprint_test.py
File metadata and controls
169 lines (165 loc) · 7.03 KB
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
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
from __future__ import division
import sys, pcap, dpkt,mlpy
import numpy as np
def train():
websitename = ['google','facebook','youtube','yahoo','baidu','wikipedia','amazon','twitter','taobao','qq','google_in','live','linkedin','sina','weibo','yahoo_jp','tmall','google_jp','ebay','t','blogspot','google_de','yandex','hao123','bing']
classlabel = []
example_count = 0
for i in range(0,25):
for j in range(1,11):
# print '=================='
incomingcount = 0
outgoingcount = 0
avg_len_incoming = 0
avg_len_outgoing = 0
avg_space_in = 0
avg_space_out = 0
pre_time = 0
incoming = False
outgoing = False
filename = '%s%d'%(websitename[i]+'_',j)
pc = pcap.pcap(filename)
for ptime,pdata in pc:
p = dpkt.ethernet.Ethernet(pdata)
ip = p.data
tcp = ip.data
sport = tcp.sport
dport = tcp.dport
if sport == 443:
outgoingcount += 1
avg_len_outgoing += len(tcp.data)
if(outgoing):
avg_space_out += ptime-pre_time
else:
pre_time = 0
pre_time = ptime
out = True
incoming = False
if dport == 443:
incomingcount += 1
avg_len_incoming += len(tcp.data)
avg_len_outgoing += len(tcp.data)
if(incoming):
avg_space_out += ptime-pre_time
else:
pre_time = 0
pre_time = ptime
out = False
incoming = True
# print incomingcount
# print avg_len_outgoing
if(incomingcount != 0 and outgoingcount!=0):
#avg_space_out = avg_space_out/outgoingcount
#avg_space_in = avg_space_in/incomingcount
avg_len_incoming = avg_len_incoming/incomingcount
avg_len_outgoing = avg_len_outgoing/outgoingcount
elif(outgoingcount!=0):
#avg_space_out = avg_space_out/outgoingcount
avg_space_in = 0
avg_len_incoming = 0
avg_len_outgoing = avg_len_ougoing/outgoingcount
elif(incomingcount!= 0):
avg_space_out = 0
#avg_space_in = avg_space_in/incomingcount
avg_len_incoming = avg_len_incoming/incomingcount
avg_len_outgoing = 0
else:
avg_space_out = 0
avg_space_in = 0
avg_len_incoming = 0
avg_len_outgoing = 0
temp = [incomingcount,outgoingcount,avg_len_incoming,avg_len_outgoing, avg_space_out]
trainset[example_count] = temp
classlabel.append(i+1)
example_count+=1
a = np.array(trainset)
a /= np.max(np.abs(a),axis=0)
b = np.array(classlabel)
knn.compute(a,b)
# print a
# print trainset
# print a.max(0)
# print a.min(0)
return 0
def predict():
correct = 0
wrong = 0
websitename = ['google','facebook','youtube','yahoo','baidu','wikipedia','amazon','twitter','taobao','qq','google_in','live','linkedin','sina','weibo','yahoo_jp','tmall','google_jp','ebay','t','blogspot','google_de','yandex','hao123','bing']
for i in range(0,25):
for j in range(1,11):
incomingcount = 0
outgoingcount = 0
avg_len_incoming = 0
avg_len_outgoing = 0
avg_space_in = 0
avg_space_out = 0
pre_time = 0
incoming = False
outgoing = False
filename = '%s%d'%(websitename[i]+'_',j)
pc = pcap.pcap(filename)
for ptime,pdata in pc:
p = dpkt.ethernet.Ethernet(pdata)
ip = p.data
tcp = ip.data
sport = tcp.sport
dport = tcp.dport
if sport == 443:
outgoingcount += 1
avg_len_outgoing += len(tcp.data)
if(outgoing):
avg_space_out += ptime-pre_time
else:
pre_time = 0
pre_time = ptime
out = True
incoming = False
if dport == 443:
incomingcount += 1
avg_len_incoming += len(tcp.data)
avg_len_outgoing += len(tcp.data)
if(incoming):
avg_space_out += ptime-pre_time
else:
pre_time = 0
pre_time = ptime
out = False
incoming = True
if(incomingcount != 0 and outgoingcount!=0):
#avg_space_out = avg_space_out/outgoingcount
#avg_space_in = avg_space_in/incomingcount
avg_len_incoming = avg_len_incoming/incomingcount
avg_len_outgoing = avg_len_outgoing/outgoingcount
elif(outgoingcount!=0):
#avg_space_out = avg_space_out/outgoingcount
avg_space_in = 0
avg_len_incoming = 0
avg_len_outgoing = avg_len_ougoing/outgoingcount
elif(incomingcount!= 0):
avg_space_out = 0
#avg_space_in = avg_space_in/incomingcount
avg_len_incoming = avg_len_incoming/incomingcount
avg_len_outgoing = 0
else:
avg_space_out = 0
avg_space_in = 0
avg_len_incoming = 0
avg_len_outgoing = 0
temp = [incomingcount,outgoingcount,avg_len_incoming,avg_len_outgoing, avg_space_out]
a = np.array(trainset)
b = a.max(0)
for i in range(0,5):
temp[i] = temp[i]/b[i]
test = np.array(temp)
if knn.predict(test) == i+1:
correct += 1
else:
wrong += 1
print '%s%d'%('# of correct: ',correct)
print '%s%d'%('# of wrong: ',wrong)
print '%s%f'%('precision: ',correct/(correct+wrong))
return 0
trainset = [[] for i in range(250)]
knn = mlpy.Knn(k=5)
train()
predict()