#-*- coding:utf-8 -*-
import re
import matplotlib.pyplot as plt
import os
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import cross_validation
import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn import linear_model,datasets
def load_one_file(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_file(path))
print "Load file(%s)" % 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_java_files(rootdir):
x=[]
y=[]
allfile=dirlist(rootdir,[])
for file in allfile:
if re.match(r"/home/qin/code/python/web-ml/1book-master/data/ADFA-LD/Attack_Data_Master/Java_Meterpreter_\d+/UAD-Java-Meterpreter*",file):
print "Load file(%s)" % file
x.append(load_one_file(file))
y.append(1)
return x,y
if __name__ == "__main__":
x1,y1 = load_adfa_training_files("/home/qin/code/python/web-ml/1book-master/data/ADFA-LD/Training_Data_Master/")
x2,y2 = load_adfa_java_files("/home/qin/code/python/web-ml/1book-master/data/ADFA-LD/Attack_Data_Master/")
x=x1+x2
y=y1+y2
vectorizer = CountVectorizer(min_df=1)
x=vectorizer.fit_transform(x)
x=x.toarray()
mlp = MLPClassifier(hidden_layer_sizes=(150,50),max_iter=10,alpha=1e-4,
solver='sgd',verbose=10,tol=1e-4,random_state=1,learning_rate_init=.1)
logreg = linear_model.LogisticRegression(C=1e5)
score=cross_validation.cross_val_score(logreg,x,y,n_jobs=-1,cv=10)
print np.mean(score)
