《Web安全之机器学习入门》笔记:第六章 6.3决策树检测POP3暴力破解

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1. 源码修改

(1)版本更改导致问题

gitbub源码为python2,改为python3

#头文件变更
from sklearn import model_selection

#调用函数变更
print(model_selection.cross_val_score(clf, x, y, n_jobs=-1, cv=10))

(2)运行报错

INTEL MKL ERROR: ҳ���ļ�̫С���޷���ɲ����� mkl_intel_thread.dll.
Intel MKL FATAL ERROR: Cannot load mkl_intel_thread.dll.
Exception in thread QueueManagerThread:
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\psutil\_pswindows.py", line 716, in wrapper
    return fun(self, *args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\psutil\_pswindows.py", line 873, in kill
    return cext.proc_kill(self.pid)
PermissionError: [WinError 5] 拒绝访问。

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\threading.py", line 926, in _bootstrap_inner
    self.run()
  File "C:\ProgramData\Anaconda3\lib\threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\externals\loky\process_executor.py", line 674, in _queue_management_worker
    recursive_terminate(p)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\externals\loky\backend\utils.py", line 26, in recursive_terminate
    _recursive_terminate_with_psutil(process)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\externals\loky\backend\utils.py", line 41, in _recursive_terminate_with_psutil
    child.kill()
  File "C:\ProgramData\Anaconda3\lib\site-packages\psutil\__init__.py", line 392, in wrapper
    return fun(self, *args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\psutil\__init__.py", line 1368, in kill
    self._proc.kill()
  File "C:\ProgramData\Anaconda3\lib\site-packages\psutil\_pswindows.py", line 718, in wrapper
    raise convert_oserror(err, pid=self.pid, name=self._name)
psutil.AccessDenied: psutil.AccessDenied (pid=42256)

修改为

print(model_selection.cross_val_score(clf, x, y, n_jobs=1, cv=10))

这里解释下参数

 n_jobs=1

进程个数,默认为1。 若值为 -1,则用所有的CPU进行运算。 若值为1,则不进行并行运算,这样的话方便调试。

3.KDD99数据集

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

4.构建特征向量

(1)仅选取pop3相关类型数据

(2)对于guess_passwd类型的数据的label标记为1

(3)对于正常数据的label标记为0

(4)特征选取0、4:8、22:30,并将其float化

def get_guess_passwdandNormal(x):
    v=[]
    w=[]
    y=[]
    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)

            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

5.完整代码

# -*- coding:utf-8 -*-

from sklearn import model_selection
from sklearn import tree
import pydotplus


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=[]
    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)

            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)
    clf = tree.DecisionTreeClassifier()
    print(model_selection.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/pop3.pdf")

6.运行结果

[0.98637602 1.         1.         1.         1.         1.
 1.         1.         1.         1.        ]

查看可视化的决策树

img

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