-
Notifications
You must be signed in to change notification settings - Fork 0
/
Traffic-Classifier.py
146 lines (133 loc) · 5.36 KB
/
Traffic-Classifier.py
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
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright (c) 2013, Jean-Rémy Bancel
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the Traffic-HMM Project nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL Jean-Rémy Bancel BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy
import pickle
import re
import sys
import os
import Classifier
def getObservationsFromTrace(obsPath, samples, step):
"""
This convert a tcpdump output to a sequence of observations
to feed Baum-Welch Algorithm
"""
obsFile = open(obsPath, 'r')
observations = []
for line in obsFile.readlines():
# Find size
m = re.search("length ([0-9]+)", line)
if m != None:
# Real Size
size = int(m.group(1))
# Sampled Size
size = min(int(size/step), samples - 1)
# Find direction
m = re.search("(ftp|http|ssh) > ", line)
if m == None:
observations.append(size)
else:
observations.append(size + samples)
# Time
time = line.split(' ')[0]
obsFile.close()
return observations
def train(path, maxSize=1000, step=10):
"""
This function trains a classifier using the data files at path
The structure should be:
path/class1/trace_file
/class2/trace_file
Such a directory structure generates a classifier with classes
class1 and class2 and train them with the trace file in the corresponding
directories
"""
# List the directory
os.chdir(path)
classes = os.listdir()
print("Training %s Classes" % len(classes))
samples = int(maxSize/step)
# Creating the classifier
Q = ["Insert1", "Server1", "Client1", "Delete1",
"Insert2", "Server2", "Client2", "Delete2"]
E = range(2*samples)
# Two-Match HMM
TM = numpy.array([
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
])
# Delete states do not emit anything
EM = numpy.array([
numpy.ones(2 * samples),
numpy.ones(2 * samples),
numpy.ones(2 * samples),
numpy.zeros(2 * samples),
numpy.ones(2 * samples),
numpy.ones(2 * samples),
numpy.ones(2 * samples),
numpy.zeros(2 * samples),
])
S = [False, False, False, True, False, False, False, True]
classifier = Classifier.Classifier(Q, E, TM=TM, EM=EM, S=S)
# Training
for className in classes:
os.chdir(className)
if len(os.listdir()) == 0:
os.chdir("..")
continue
print("Training class", className, "with", len(os.listdir()), "samples")
# Setting random parameters before training
classifier.addClass(className)
classifier.resetClass(className)
# Training the class on all the observations in the directory
for obsPath in os.listdir():
print("Training with", obsPath)
classifier.trainClass(className,
getObservationsFromTrace(obsPath, samples, step))
os.chdir("..")
os.chdir("..")
# Saving the classifier
dump = open("HMM.dump", 'bw')
pickle.dump(classifier, dump)
dump.close()
def classify(HMMpath, tracePath, maxSize=1000, step=10):
samples = int(maxSize/step)
HMMFile = open("HMM.dump", 'rb')
classifier = pickle.load(HMMFile)
print(classifier.classify(getObservationsFromTrace(tracePath, samples, step)))
if __name__ == "__main__":
if len(sys.argv) < 3:
raise "Not enough arguments"
if sys.argv[1] == "train":
train(sys.argv[2])
elif sys.argv[1] == "classify":
classify(sys.argv[2], sys.argv[3])