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feature_extract.py
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#coding=utf-8
from collections import Counter
import xlwt
from math import log
"""
提取特征包括：
1.元音字母个数所占比例
2.数字个数所占比例
3.包含不同字母个数所占比例
4.包含不同字母的个数
5.信息熵
6.重复字母个数所占比例
7.连续辅音字母所占比例
8.最小字符与最大字符的ASCII差值
9.是否包含数字
"""
def vowel_ratio(domain, lenth): #元音字母个数所占比例
tmp = 0.0
vowels = ['a', 'e', 'i', 'o', 'u']
for alpha in domain:
if alpha in vowels:
tmp += 1.0
ratio = tmp / lenth
return ratio
def digital_ratio(domain, lenth): #数字个数所占比例
tmp = 0.0
for alpha in domain:
if alpha.isdigit():
tmp += 1.0
ratio = tmp / lenth
return ratio
def char_diversity(domain, lenth): #包含不同字母个数所占比例
tmp = 0.0
alphas = []
for alpha in domain:
if alpha not in alphas and alpha.isalpha():
alphas.append(alpha)
tmp += 1.0
ratio = tmp / lenth
return ratio
def unialpha_ratio(domain): #包含不同字母的个数
tmp = 0.0
alphas = []
for alpha in domain:
if alpha not in alphas and alpha.isalpha():
alphas.append(alpha)
tmp += 1.0
return tmp
def shannon_entropy(domain, lenth): #计算信息熵
numEntires = lenth
labelCounts = {}
for featVec in domain:
currentLabel = featVec[-1]
if currentLabel not in labelCounts.keys():
labelCounts[currentLabel] = 0
labelCounts[currentLabel] += 1
shannonEnt = 0.0
for key in labelCounts:
prob = float(labelCounts[key]) / numEntires
shannonEnt -= prob * log(prob, 2)
return shannonEnt
def ratio_of_repeated_chars(domain, lenth): #重复字母个数所占比例
alphas = []
sum = 0.0
for alpha in domain:
alphas.append(alpha)
count = Counter(alphas)
for key,value in count.items():
if value > 1:
sum += value
print(count)
print(sum)
ratio = sum / lenth
return ratio
def consecutive_consonant_ration(domain, lenth): #连续辅音字母所占比例
tmp = 0.0
maxConsec = 0.0
vowels = ['a', 'e', 'i', 'o', 'u']
for alpha in domain:
if alpha not in vowels and alpha.isalpha():
tmp += 1.0
if tmp > maxConsec: maxConsec = tmp
else:
tmp = 0.0
ratio = maxConsec / lenth
return ratio
def start2end_asc(domain): #最小字符与最大字符的ASCII差值
domain = domain.strip()
minAsc, maxAsc = 122.0, 0.0
for alpha in domain:
print('alpha: {}, asc: {}'.format(alpha, ord(alpha)))
if ord(alpha) > maxAsc:
maxAsc = ord(alpha)
if ord(alpha) < minAsc:
minAsc = ord(alpha)
return maxAsc - minAsc
def contains_digital(domain): #是否包含数字
for alpha in domain:
if alpha.isdigit():
return 1
else:
return 0
def main(file): #返回提取的特征数据
table = xlwt.Workbook(encoding='utf-8')
sheet = table.add_sheet('feature_data')
sheet.write(0, 0, label='元音字母所占比例')
sheet.write(0, 1, label='数字个数所占比例')
sheet.write(0, 2, label='不同字母个数所占比例')
sheet.write(0, 3, label='包含不同字母的个数')
sheet.write(0, 4, label='信息熵')
sheet.write(0, 5, label='重复字母个数所占比例')
sheet.write(0, 6, label='连续辅音字母所占比例')
sheet.write(0, 7, label='ASC码差值')
sheet.write(0, 8, label='是否包含数字')
with open(file, 'r') as file:
domains = file.readlines()
tmp = 1
for domain in domains:
domain = domain[:-5]
lenth = len(domain)
vowel = vowel_ratio(domain, lenth)
digital = digital_ratio(domain, lenth)
char_div = char_diversity(domain, lenth)
uni = unialpha_ratio(domain)
shannon = shannon_entropy(domain, lenth)
repeated = ratio_of_repeated_chars(domain, lenth)
consonant = consecutive_consonant_ration(domain, lenth)
sta2end = start2end_asc(domain)
contain_dig = contains_digital(domain)
sheet.write(tmp, 0, label=vowel)
sheet.write(tmp, 1, label=digital)
sheet.write(tmp, 2, label=char_div)
sheet.write(tmp, 3, label=uni)
sheet.write(tmp, 4, label=shannon)
sheet.write(tmp, 5, label=repeated)
sheet.write(tmp, 6, label=consonant)
sheet.write(tmp, 7, label=sta2end)
sheet.write(tmp, 8, label=contain_dig)
tmp += 1
table.save(r'C:\Users\Administrator\Desktop\DNS恶意域名检测\2020Datacon.dns恶意域名\dns_2_question\feature_data.xlsx')
if __name__ == '__main__':
malicous_domains = r'C:\Users\Administrator\Desktop\DNS恶意域名检测\2020Datacon.dns恶意域名\dns_2_question\malicious.txt'
main(malicous_domains)