DGA ngram kmeans+TSNE用于绘图
2024-09-08 10:40:45
# -*- coding:utf-8 -*- import sys
import re
import numpy as np
from sklearn.externals import joblib
import csv
import matplotlib.pyplot as plt
import os
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import cross_validation
import os
from sklearn.naive_bayes import GaussianNB
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE #处理域名的最小长度
MIN_LEN=10 #随机程度
random_state = 170 def load_alexa(filename):
domain_list=[]
csv_reader = csv.reader(open(filename))
for row in csv_reader:
domain=row[1]
if domain >= MIN_LEN:
domain_list.append(domain)
return domain_list def load_dga(filename):
domain_list=[]
#xsxqeadsbgvpdke.co.uk,Domain used by Cryptolocker - Flashback DGA for 13 Apr 2017,2017-04-13,
# http://osint.bambenekconsulting.com/manual/cl.txt
with open(filename) as f:
for line in f:
domain=line.split(",")[0]
if domain >= MIN_LEN:
domain_list.append(domain)
return domain_list def nb_dga():
x1_domain_list = load_alexa("../data/top-1000.csv")
x2_domain_list = load_dga("../data/dga-cryptolocke-1000.txt")
x3_domain_list = load_dga("../data/dga-post-tovar-goz-1000.txt") x_domain_list=np.concatenate((x1_domain_list, x2_domain_list,x3_domain_list)) y1=[0]*len(x1_domain_list)
y2=[1]*len(x2_domain_list)
y3=[2]*len(x3_domain_list) y=np.concatenate((y1, y2,y3)) print x_domain_list
cv = CountVectorizer(ngram_range=(2, 2), decode_error="ignore",
token_pattern=r"\w", min_df=1)
x= cv.fit_transform(x_domain_list).toarray() clf = GaussianNB()
print cross_validation.cross_val_score(clf, x, y, n_jobs=-1, cv=3) def kmeans_dga():
x1_domain_list = load_alexa("../data/dga/top-100.csv")
x2_domain_list = load_dga("../data/dga/dga-cryptolocke-50.txt")
x3_domain_list = load_dga("../data/dga/dga-post-tovar-goz-50.txt") x_domain_list=np.concatenate((x1_domain_list, x2_domain_list,x3_domain_list))
#x_domain_list = np.concatenate((x1_domain_list, x2_domain_list)) y1=[0]*len(x1_domain_list)
y2=[1]*len(x2_domain_list)
y3=[1]*len(x3_domain_list) y=np.concatenate((y1, y2,y3))
#y = np.concatenate((y1, y2)) #print x_domain_list cv = CountVectorizer(ngram_range=(2, 2), decode_error="ignore",
token_pattern=r"\w", min_df=1)
x= cv.fit_transform(x_domain_list).toarray()
model=KMeans(n_clusters=2, random_state=random_state)
y_pred = model.fit_predict(x)
#print y_pred tsne = TSNE(learning_rate=100)
x=tsne.fit_transform(x)
print x
print x_domain_list for i,label in enumerate(x):
#print label
x1,x2=x[i]
if y_pred[i] == 1:
plt.scatter(x1,x2,marker='o')
else:
plt.scatter(x1, x2,marker='x')
#plt.annotate(label,xy=(x1,x2),xytext=(x1,x2)) plt.show() if __name__ == '__main__':
#nb_dga()
kmeans_dga()
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