检测用户命令序列异常——使用LSTM分类算法
Posted bonelee
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了检测用户命令序列异常——使用LSTM分类算法相关的知识，希望对你有一定的参考价值。
通过 搜集 Linux 服务器 的 bash 操作 日志， 通过 训练 识别 出 特定 用户 的 操作 习惯， 然后 进一步 识别 出 异常 操作 行为。
使用 SEA 数据 集 涵盖 70 多个 UNIX 系统 用户 的 行为 日志， 这些 数据 来自 UNIX 系统 acct 机制 记录 的 用户 使用 的 命令。 SEA 数据 集中 每个 用户 都 采集 了 15000 条 命令， 从 用户 集合 中 随机 抽取 50 个 用户 作为 正常 用户， 剩余 用户 的 命令 块 中 随机 插入 模拟 命令 作为 内部 伪装 者 攻击 数据。其中 训练 集合 大小 为 80， 测试 集合 大小 为 70。
数据集示意：
cpp sh xrdb cpp sh xrdb mkpts test stty hostname date echo [ find chmod tty echo env echo sh userenv wait4wm xhost xsetroot reaper xmodmap sh [ cat stty hostname date echo [ find chmod tty echo sh more sh more sh more sh more sh more sh more sh more sh more sh more sh more sh more sh launchef launchef sh 9term sh launchef sh launchef hostname [ cat stty hostname date echo [ find chmod tty echo sh more sh more sh ex sendmail sendmail sh MediaMai sendmail sh rm MediaMai sh rm MediaMai launchef launchef sh sh more sh sh rm MediaMai netstat netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape netscape sh netscape more sh rm sh MediaMai = telnet tput netscape netscape netscape netscape netscape
# -*- coding:utf-8 -*-
import sys
import re
import numpy as np
import nltk
import csv
import matplotlib.pyplot as plt
from nltk.probability import FreqDist
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import cross_validation
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb
import tflearn
#测试样本数
N=80
def load_user_cmd_new(filename):
cmd_list=[]
dist=[]
with open(filename) as f:
i=0
x=[]
for line in f:
line=line.strip(‘
‘)
x.append(line)
dist.append(line)
i+=1
if i == 100:
cmd_list.append(x)
x=[]
i=0
fdist = FreqDist(dist).keys()
return cmd_list,fdist
def load_user_cmd(filename):
cmd_list=[]
dist_max=[]
dist_min=[]
dist=[]
with open(filename) as f:
i=0
x=[]
for line in f:
line=line.strip(‘
‘)
x.append(line)
dist.append(line)
i+=1
if i == 100:
cmd_list.append(x)
x=[]
i=0
fdist = FreqDist(dist).keys()
dist_max=set(fdist[0:50])
dist_min = set(fdist[-50:])
return cmd_list,dist_max,dist_min
def get_user_cmd_feature(user_cmd_list,dist_max,dist_min):
user_cmd_feature=[]
for cmd_block in user_cmd_list:
f1=len(set(cmd_block))
fdist = FreqDist(cmd_block).keys()
f2=fdist[0:10]
f3=fdist[-10:]
f2 = len(set(f2) & set(dist_max))
f3=len(set(f3)&set(dist_min))
x=[f1,f2,f3]
user_cmd_feature.append(x)
return user_cmd_feature
def get_user_cmd_feature_new(user_cmd_list,dist):
user_cmd_feature=[]
for cmd_list in user_cmd_list:
x=[]
for cmd in cmd_list:
v = [0] * len(dist)
for i in range(0, len(dist)):
if cmd == dist[i]:
v[i] = 1
x.append(v)
user_cmd_feature.append(x)
return user_cmd_feature
def get_label(filename,index=0):
x=[]
with open(filename) as f:
for line in f:
line=line.strip(‘
‘)
x.append( int(line.split()[index]))
return x
def do_knn(x_train,y_train,x_test,y_test):
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(x_train, y_train)
y_predict=neigh.predict(x_test)
score = np.mean(y_test == y_predict) * 100
print score
def do_rnn(x_train,x_test,y_train,y_test):
global n_words
# Data preprocessing
# Sequence padding
print "GET n_words embedding %d" % n_words
#x_train = pad_sequences(x_train, maxlen=100, value=0.)
#x_test = pad_sequences(x_test, maxlen=100, value=0.)
# Converting labels to binary vectors
y_train = to_categorical(y_train, nb_classes=2)
y_test = to_categorical(y_test, nb_classes=2)
# Network building
net = tflearn.input_data(shape=[None, 100,n_words])
net = tflearn.lstm(net, 10, return_seq=True)
net = tflearn.lstm(net, 10, )
net = tflearn.fully_connected(net, 2, activation=‘softmax‘)
net = tflearn.regression(net, optimizer=‘adam‘, learning_rate=0.1,name="output",
loss=‘categorical_crossentropy‘)
# Training
model = tflearn.DNN(net, tensorboard_verbose=3)
model.fit(x_train, y_train, validation_set=(x_test, y_test), show_metric=True,
batch_size=32,run_id="maidou")
if __name__ == ‘__main__‘:
user_cmd_list,dist=load_user_cmd_new("../data/MasqueradeDat/User7")
#print "Dist:(%s)" % dist
n_words=len(dist)
user_cmd_feature=get_user_cmd_feature_new(user_cmd_list,dist)
labels=get_label("../data/MasqueradeDat/label.txt",6)
y=[0]*50+labels
x_train=user_cmd_feature[0:N]
y_train=y[0:N]
x_test=user_cmd_feature[N:150]
y_test=y[N:150]
#print x_train
do_rnn(x_train,x_test,y_train,y_test)
效果：
Training Step: 30 | total loss: 0.10088 | time: 1.185s
| Adam | epoch: 010 | loss: 0.10088 - acc: 0.9591 | val_loss: 0.18730 - val_acc: 0.9571 -- iter: 80/80
--
以上是关于检测用户命令序列异常——使用LSTM分类算法的主要内容，如果未能解决你的问题，请参考以下文章
用于时间序列异常检测的 Keras LSTM-VAE（变分自动编码器）