# -*- 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 sklearn
import osimport numpy as np
from sklearn.neural_network import MLPClassifierdef load_one_flle(filename):x=[]with open(filename) as f:line=f.readline()line=line.strip('\n')return linedef 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))print "Load file(%s)" % pathy.append(0)return x,ydef 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 allfiledef load_adfa_java_files(rootdir):x=[]y=[]allfile=dirlist(rootdir,[])for file in allfile:if re.match(r"../data/ADFA-LD/Attack_Data_Master/Java_Meterpreter_\d+/UAD-Java-Meterpreter*",file):print "Load file(%s)" % filex.append(load_one_flle(file))y.append(1)return x,yif __name__ == '__main__':x1,y1=load_adfa_training_files("../data/ADFA-LD/Training_Data_Master/")x2,y2=load_adfa_java_files("../data/ADFA-LD/Attack_Data_Master/")x=x1+x2y=y1+y2#print xvectorizer = CountVectorizer(min_df=1)x=vectorizer.fit_transform(x)x=x.toarray()    print x[:10]min_max_scaler = sklearn.preprocessing.MinMaxScaler()x = min_max_scaler.fit_transform(x)print x[:10]mlp = MLPClassifier(hidden_layer_sizes=(150,50), max_iter=30, alpha=1e-4,solver='sgd', verbose=10, tol=1e-4, random_state=1,learning_rate_init=.1)score=cross_validation.cross_val_score(mlp, x, y, n_jobs=-1, cv=10)print  np.mean(score)