引入一个概念，杰卡德距离(Jaccard Distance)，是用来衡量两个集合差异性的一种指标。两个集合交集的元素个数除以并集的元素个数。
Jaccard index[1], 又称为Jaccard相似系数（Jaccard similarity coefficient）用于比较有限样本集之间的相似性与差异性。Jaccard系数值越大，样本相似度越高。
隐马尔可夫模型（Hidden Markov Model，HMM）是统计模型，它用来描述一个含有隐含未知参数的马尔可夫过程。其难点是从可观察的参数中确定该过程的隐含参数。然后利用这些参数来作进一步的分析，例如模式识别。
#-*- coding:utf-8 -*-
import sys
import urllib
import urlparse
import re
from hmmlearn import hmm #导出隐马尔可夫模型
import numpy as np
from sklearn.externals import joblib
import HTMLParser
import nltk
import csv
import matplotlib.pyplot as plt
import os
#处理域名的最小长度
MIN_LEN=10
#状态个数
N=8
#最大似然概率阈值
T=-50
FILE_MODEL="9-2.m"
#从CSV文件里逐行读取 出 每一行的第二列元素 取出域名数据
def load_alexa(filename):
domain_list=[]
csv_reader = csv.reader(open(filename))
for row in csv_reader:
domain=row[1]
if len(domain) >= MIN_LEN:
domain_list.append(domain)
return domain_list
#chr()函数用一个范围在range（256）内的（就是0～255）整数作参数，返回一个对应的字符。unichr()跟它一样，只不过返回的是Unicode字符
#ord()函数是chr()函数（对于8位的ASCII字符串）或unichr()函数（对于Unicode对象）的配对函数，它以一个字符（长度为1的字符串）作为参数，返回对应的ASCII数值，或者Unicode数值
def domain2ver(domain):
ver=[]
for i in range(0,len(domain)):
ver.append([ord(domain[i])])
return ver
#numpy提供了numpy.concatenate((a1,a2,...), axis=0)函数。能够一次完成多个数组的拼接。其中a1,a2,...是数组类型的参数
#请参考这个教程：http://blog.csdn.net/zyl1042635242/article/details/43162031
def train_hmm(domain_list):
x = [[0]]
x_lens = [1]
for domain in domain_list:
ver=domain2ver(domain)
np_ver = np.array(ver)
x=np.concatenate([x,np_ver])
x_lens.append(len(np_ver))
remodel = hmm.GaussianHMM(n_components=N,covariance_type="full",n_iter=100)
remodel.fit(x,x_lens)
joblib.dump(remodel,FILE_MODEL)
return remodel
def load_dga(filename):
domain_list=[]
with open(filename) as f:
for line in f:
domain=line.split(",")[0]
if len(domain) >= MIN_LEN:
domain_list.append(domain)
return domain_list
def test_dga(remodel,filename):
x=[]
y=[]
dga_cryptolocke_list = load_dga(filename)
for domain in dga_cryptolocke_list:
domain_ver = domain2ver(domain)
np_ver = np.array(domain_ver)
pro = remodel.score(np_ver)
x.append(len(domain))
y.append(pro)
return x,y
def test_alexa(remodel,filename):
x=[]
y=[]
alexa_list = load_alexa(filename)
for domain in alexa_list:
domain_ver=domain2ver(domain)
np_ver = np.array(domain_ver)
pro = remodel.score(np_ver)
#print "SCORE:(%d) DOMAIN:(%s) " % (pro, domain)
x.append(len(domain))
y.append(pro)
return x, y
def show_hmm():
domain_list = load_alexa("/home/qin/code/python/web-ml/1book-master/data/top-1000.csv")
if not os.path.exists(FILE_MODEL):
remodel=train_hmm(domain_list)
remodel=joblib.load(FILE_MODEL)
x_3,y_3=test_dga(remodel, "/home/qin/code/python/web-ml/1book-master/data/dga-post-tovar-goz-1000.txt")
x_2,y_2=test_dga(remodel,"/home/qin/code/python/web-ml/1book-master/data/dga-cryptolocke-1000.txt")
x_1,y_1=test_alexa(remodel, "/home/qin/code/python/web-ml/1book-master/data/test-top-1000.csv")
fig,ax=plt.subplots()
ax.set_xlabel('Domain Length')
ax.set_ylabel('HMM Score')
ax.scatter(x_3,y_3,color='b',label="dga_post-tovar-goz",marker='o')
ax.scatter(x_2, y_2, color='g', label="dga_cryptolock",marker='v')
ax.scatter(x_1, y_1, color='r', label="alexa",marker='*')
ax.legend(loc='best')
plt.show()
def get_aeiou(domain_list):
x=[]
y=[]
for domain in domain_list:
x.append(len(domain))
count=len(re.findall(r'[aeiou]',domain.lower()))
count=(0.0+count)/len(domain)
y.append(count)
return x,y
def show_aeiou():
x1_domain_list = load_alexa("/home/qin/code/python/web-ml/1book-master/data/top-1000.csv")
x_1,y_1=get_aeiou(x1_domain_list)
x2_domain_list = load_dga("/home/qin/code/python/web-ml/1book-master/data/dga-cryptolocke-1000.txt")
x_2,y_2=get_aeiou(x2_domain_list)
x3_domain_list = load_dga("/home/qin/code/python/web-ml/1book-master/data/dga-post-tovar-goz-1000.txt")
x_3,y_3=get_aeiou(x3_domain_list)
fig,ax=plt.subplots()
ax.set_xlabel('Domain Length')
ax.set_ylabel('AEIOU Score')
ax.scatter(x_3,y_3,color='b',label="dga_post-tovar-goz",marker='o')
ax.scatter(x_2, y_2, color='g', label="dga_cryptolock",marker='v')
ax.scatter(x_1, y_1, color='r', label="alexa",marker='*')
ax.legend(loc='best')
plt.show()
def get_uniq_char_num(domain_list):
x=[]
y=[]
for domain in domain_list:
x.append(len(domain))
count=len(set(domain))
count=(0.0+count)/len(domain)
y.append(count)
return x,y
def show_uniq_char_num():
x1_domain_list = load_alexa("/home/qin/code/python/web-ml/1book-master/data/top-1000.csv")
x_1,y_1=get_uniq_char_num(x1_domain_list)
x2_domain_list = load_dga("/home/qin/code/python/web-ml/1book-master/data/dga-cryptolocke-1000.txt")
x_2,y_2=get_uniq_char_num(x2_domain_list)
x3_domain_list = load_dga("/home/qin/code/python/web-ml/1book-master/data/dga-post-tovar-goz-1000.txt")
x_3,y_3=get_uniq_char_num(x3_domain_list)
fig,ax=plt.subplots()
ax.set_xlabel('Domain Length')
ax.set_ylabel('UNIQ CHAR NUMBER')
ax.scatter(x_3,y_3,color='b',label="dga_post-tovar-goz",marker='o')
ax.scatter(x_2, y_2, color='g', label="dga_cryptolock",marker='v')
ax.scatter(x_1, y_1, color='r', label="alexa",marker='*')
ax.legend(loc='best')
plt.show()
def count2string_jarccard_index(a,b):
x=set(' '+a[0])
y=set(' '+b[0])
for i in range(0,len(a)-1):
x.add(a[i]+a[i+1])
x.add(a[len(a)-1]+' ')
for i in range(0,len(b)-1):
y.add(b[i]+b[i+1])
y.add(b[len(b)-1]+' ')
return (0.0+len(x-y))/len(x|y)
def get_jarccard_index(a_list,b_list):
x=[]
y=[]
for a in a_list:
j=0.0
for b in b_list:
j+=count2string_jarccard_index(a,b)
x.append(len(a))
y.append(j/len(b_list))
return x,y
def show_jarccard_index():
x1_domain_list = load_alexa("/home/qin/code/python/web-ml/1book-master/data/top-1000.csv")
x_1,y_1=get_jarccard_index(x1_domain_list,x1_domain_list)
x2_domain_list = load_dga("/home/qin/code/python/web-ml/1book-master/data/dga-cryptolocke-1000.txt")
x_2,y_2=get_jarccard_index(x2_domain_list,x1_domain_list)
x3_domain_list = load_dga("/home/qin/code/python/web-ml/1book-master/data/dga-post-tovar-goz-1000.txt")
x_3,y_3=get_jarccard_index(x3_domain_list,x1_domain_list)
fig,ax=plt.subplots()
ax.set_xlabel('Domain Length')
ax.set_ylabel('JARCCARD INDEX')
ax.scatter(x_3,y_3,color='b',label="dga_post-tovar-goz",marker='o')
ax.scatter(x_2, y_2, color='g', label="dga_cryptolock",marker='v')
ax.scatter(x_1, y_1, color='r', label="alexa",marker='*')
ax.legend(loc='lower right')
plt.show()
if __name__ == '__main__':
show_jarccard_index()
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