目录
简介
决策树简单用法
决策树检测P0P3爆破
决策树检测FTP爆破
随机森林检测FTP爆破
简介
决策树和随机森林算法是最常见的分类算法；
决策树，判断的逻辑很多时候和人的思维非常接近。
随机森林算法，利用多棵决策树对样本进行训练并预测的一种分类器，并且其输出的类别是由个别决策树输出的类别的众数决定。
决策树简单用法
使用sklearn自带的iris数据集
代码语言：javascript复制# -*- coding: utf-8 -*-
from sklearn.datasets import load_iris
from sklearn import tree
import pydotplus
"""
如果报错GraphViz's executables not found，手动添加环境变量
"""
import os
os.environ["PATH"] = os.pathsep 'D:/Program Files (x86)/Graphviz2.38/bin/' #注意修改你的路径iris = load_iris()
clf = tree.DecisionTreeClassifier()
clf = clf.fit(iris.data, iris.target)
#可视化训练得到的决策树
dot_data = tree.export_graphviz(clf, out_file=None)
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_pdf("../photo/6/iris.pdf")
决策树算法检测P0P3爆破
代码语言：javascript复制# -*- coding:utf-8 -*-
import re
from sklearn import cross_validation
from sklearn import tree
import pydotplus
import os
os.environ["PATH"] = os.pathsep 'D:/Program Files (x86)/Graphviz2.38/bin/' #注意修改你的路径
"""
收集并清洗数据
"""
def load_kdd99(filename):
x=[]
with open(filename) as f:
for line in f:
line=line.strip('n')
line=line.split(',')
x.append(line)
return x
def get_guess_passwdandNormal(x):
v=[]
w=[]
y=[]
"""
筛选标记为guess-passwd和normal且是P0P3协议的数据
"""
for x1 in x:
if ( x1[41] in ['guess_passwd.','normal.'] ) and ( x1[2] == 'pop_3' ):
if x1[41] == 'guess_passwd.':
y.append(1)
else:
y.append(0)
"""
特征化
挑选与p0p3密码破解相关的网络特征以及TCP协议内容的特征作为样本特征
"""
x1 = [x1[0]] x1[4:8] x1[22:30]
v.append(x1)
for x1 in v :
v1=[]
for x2 in x1:
v1.append(float(x2))
w.append(v1)
return w,y
if __name__ == '__main__':
v=load_kdd99("../data/kddcup99/corrected")
x,y=get_guess_passwdandNormal(v)
"""
训练样本
实例化决策树算法
"""
clf = tree.DecisionTreeClassifier()
#十折交叉验证
print(cross_validation.cross_val_score(clf, x, y, n_jobs=-1, cv=10))
clf = clf.fit(x, y)
dot_data = tree.export_graphviz(clf, out_file=None)
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_pdf("../photo/6/iris-dt.pdf")准确率达到99%
代码语言：javascript复制[ 0.98637602 1. 1. 1. 1. 1. 1.
1. 1. 1. ]可视化结果
决策树算法检测FTP爆破
代码语言：javascript复制# -*- coding:utf-8 -*-
import re
import os
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import cross_validation
import os
from sklearn import tree
import pydotplus
"""
"""
def load_one_flle(filename):
x=[]
with open(filename) as f:
line=f.readline()
line=line.strip('n')
return line
"""
加载ADFA-LD中的正常样本数据
"""
def load_adfa_training_files(rootdir):
x=[]
y=[]
list = os.listdir(rootdir)
for i in range(0, len(list)):
path = os.path.join(rootdir, list[i])
if os.path.isfile(path):
x.append(load_one_flle(path))
y.append(0)
return x,y
"""
定义遍历目录下文件的函数，作为load_adfa_hydra_ftp_files的子函数
"""
def dirlist(path, allfile):
filelist = os.listdir(path)
for filename in filelist:
filepath = os.path.join(path, filename)
if os.path.isdir(filepath):
dirlist(filepath, allfile)
else:
allfile.append(filepath)
return allfile
"""
从攻击数据集中筛选和FTP爆破相关的数据
"""
def load_adfa_hydra_ftp_files(rootdir):
x=[]
y=[]
allfile=dirlist(rootdir,[])
for file in allfile:
"""
rootdir下有多个文件，多个文件里又有多个文件
"""
if re.match(r"../data/ADFA-LD/Attack_Data_Master/Hydra_FTP_d \UAD-Hydra-FTP*",file):
x.append(load_one_flle(file))
y.append(1)
return x,y
if __name__ == '__main__':
"""
特征化
由于ADFA-LD数据集都记录了函数调用的序列，每个文件包含的函数调用序列的个数都不一致
"""
x1,y1=load_adfa_training_files("../data/ADFA-LD/Training_Data_Master/")
#x1{2184×833} y1{833}
x2,y2=load_adfa_hydra_ftp_files("../data/ADFA-LD/Attack_Data_Master/")
#x2{524×162} y2{162}
x=x1 x2
y=y1 y2
#x{2184×995} y{955}
vectorizer = CountVectorizer(min_df=1)
#min_df如果某个词的document frequence小于min_df，则这个词不会被当作关键词
x=vectorizer.fit_transform(x)
x=x.toarray()
#x{142×955}
#实例化决策树算法
clf = tree.DecisionTreeClassifier()
#效果验证
print(cross_validation.cross_val_score(clf, x, y, n_jobs=-1, cv=10))
clf = clf.fit(x, y)
dot_data = tree.export_graphviz(clf, out_file=None)
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_pdf("../photo/6/ftp.pdf")[ 1. 0.98019802 0.95 0.97979798 0.96969697 0.88888889
0.98989899 0.95959596 0.92929293 0.95959596]
随机森林算法检测FTP爆破
代码语言：javascript复制# -*- coding:utf-8 -*-
#pydotplus只支持决策树
import re
import os
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import cross_validation
import os
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
import numpy as np
def load_one_flle(filename):
x=[]
with open(filename) as f:
line=f.readline()
line=line.strip('n')
return line
def load_adfa_training_files(rootdir):
x=[]
y=[]
list = os.listdir(rootdir)
for i in range(0, len(list)):
path = os.path.join(rootdir, list[i])
if os.path.isfile(path):
x.append(load_one_flle(path))
y.append(0)
return x,y
def dirlist(path, allfile):
filelist = os.listdir(path)
for filename in filelist:
filepath = os.path.join(path, filename)
if os.path.isdir(filepath):
dirlist(filepath, allfile)
else:
allfile.append(filepath)
return allfile
def load_adfa_hydra_ftp_files(rootdir):
x=[]
y=[]
allfile=dirlist(rootdir,[])
for file in allfile:
if re.match(r"../data/ADFA-LD/Attack_Data_Master/Hydra_FTP_d \UAD-Hydra-FTP*",file):
x.append(load_one_flle(file))
y.append(1)
return x,y
if __name__ == '__main__':
x1,y1=load_adfa_training_files("../data/ADFA-LD/Training_Data_Master/")
x2,y2=load_adfa_hydra_ftp_files("../data/ADFA-LD/Attack_Data_Master/")
x=x1 x2
y=y1 y2
#print(x)
vectorizer = CountVectorizer(min_df=1)
x=vectorizer.fit_transform(x)
x=x.toarray()
#print(y)
#选用决策树分类器
clf1 = tree.DecisionTreeClassifier()
score=cross_validation.cross_val_score(clf1, x, y, n_jobs=-1, cv=10)
print('决策树',np.mean(score))
#选用随机森林分类器
clf2 = RandomForestClassifier(n_estimators=10, max_depth=None,min_samples_split=2, random_state=0)
score=cross_validation.cross_val_score(clf2, x, y, n_jobs=-1, cv=10)
print('随机森林',np.mean(score))决策树 0.955736173617
随机森林 0.984888688869
