您或许知道,作者后续分享网络安全的文章会越来越少。但如果您想学习人工智能和安全结合的应用,您就有福利了,作者将重新打造一个《当人工智能遇上安全》系列博客,详细介绍人工智能与安全相关的论文、实践,并分享各种案例,涉及恶意代码检测、恶意请求识别、入侵检测、对抗样本等等。只想更好地帮助初学者,更加成体系的分享新知识。该系列文章会更加聚焦,更加学术,更加深入,也是作者的慢慢成长史。换专业确实挺难的,系统安全也是块硬骨头,但我也试试,看看自己未来四年究竟能将它学到什么程度,漫漫长征路,偏向虎山行。享受过程,一起加油~
前文讲解如何实现威胁情报实体识别,利用BiLSTM-CRF算法实现对ATT&CK相关的技战术实体进行提取,是安全知识图谱构建的重要支撑。这篇文章将以中文语料为主,介绍中文命名实体识别研究,并构建BiGRU-CRF模型实现。基础性文章,希望对您有帮助,如果存在错误或不足之处,还请海涵。且看且珍惜!
由于上一篇文章详细讲解ATT&CK威胁情报采集、预处理、BiLSTM-CRF实体识别内容,这篇文章不再详细介绍,本文将在上一篇文章基础上补充:
- 中文命名实体识别如何实现,以字符为主
- 以中文CSV文件为语料,介绍其处理过程,中文威胁情报类似
- 构建BiGRU-CRF模型实现中文实体识别
版本信息:
- keras-contrib V2.0.8
- keras V2.3.1
- tensorflow V2.2.0
常见框架如下图所示:
文章目录
作者作为网络安全的小白,分享一些自学基础教程给大家,主要是在线笔记,希望您们喜欢。同时,更希望您能与我一起操作和进步,后续将深入学习AI安全和系统安全知识并分享相关实验。总之,希望该系列文章对博友有所帮助,写文不易,大神们不喜勿喷,谢谢!如果文章对您有帮助,将是我创作的最大动力,点赞、评论、私聊均可,一起加油喔!
前文推荐:
- [当人工智能遇上安全] 1.人工智能真的安全吗?浙大团队外滩大会分享AI对抗样本技术
- [当人工智能遇上安全] 2.清华张超老师 - GreyOne: Discover Vulnerabilities with Data Flow Sensitive Fuzzing
- [当人工智能遇上安全] 3.安全领域中的机器学习及机器学习恶意请求识别案例分享
- [当人工智能遇上安全] 4.基于机器学习的恶意代码检测技术详解
- [当人工智能遇上安全] 5.基于机器学习算法的主机恶意代码识别研究
- [当人工智能遇上安全] 6.基于机器学习的入侵检测和攻击识别------以KDD CUP99数据集为例
- [当人工智能遇上安全] 7.基于机器学习的安全数据集总结
- [当人工智能遇上安全] 8.基于API序列和机器学习的恶意家族分类实例详解
- [当人工智能遇上安全] 9.基于API序列和深度学习的恶意家族分类实例详解
- [当人工智能遇上安全] 10.威胁情报实体识别之基于BiLSTM-CRF的实体识别万字详解
- [当人工智能遇上安全] 11.威胁情报实体识别 (2)基于BiGRU-CRF的中文实体识别万字详解
作者的github资源:
- https://github.com/eastmountyxz/When-AI-meet-Security
- https://github.com/eastmountyxz/AI-Security-Paper
一.ATT&CK数据采集
了解威胁情报的同学,应该都熟悉Mitre的ATT&CK网站,前文已介绍如何采集该网站APT组织的攻击技战术数据。网址如下:
第一步,通过ATT&CK网站源码分析定位APT组织名称,并进行系统采集。
安装BeautifulSoup扩展包,该部分代码如下所示:
python
#encoding:utf-8
#By:Eastmount CSDN
import re
import requests
from lxml import etree
from bs4 import BeautifulSoup
import urllib.request
#-------------------------------------------------------------------------------------------
#获取APT组织名称及链接
#设置浏览器代理,它是一个字典
headers = {
'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) \
AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36'
}
url = 'https://attack.mitre.org/groups/'
#向服务器发出请求
r = requests.get(url = url, headers = headers).text
#解析DOM树结构
html_etree = etree.HTML(r)
names = html_etree.xpath('//*[@class="table table-bordered table-alternate mt-2"]/tbody/tr/td[2]/a/text()')
print (names)
print(len(names),names[0])
filename = []
for name in names:
filename.append(name.strip())
print(filename)
#链接
urls = html_etree.xpath('//*[@class="table table-bordered table-alternate mt-2"]/tbody/tr/td[2]/a/@href')
print(urls)
print(len(urls), urls[0])
print("\n")
此时输出结果如下图所示,包括APT组织名称及对应的URL网址。
第二步,访问APT组织对应的URL,采集详细信息(正文描述)。
第三步,采集对应的技战术TTPs信息,其源码定位如下图所示。
第四步,编写代码完成威胁情报数据采集。01-spider-mitre.py 完整代码如下:
python
#encoding:utf-8
#By:Eastmount CSDN
import re
import requests
from lxml import etree
from bs4 import BeautifulSoup
import urllib.request
#-------------------------------------------------------------------------------------------
#获取APT组织名称及链接
#设置浏览器代理,它是一个字典
headers = {
'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) \
AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36'
}
url = 'https://attack.mitre.org/groups/'
#向服务器发出请求
r = requests.get(url = url, headers = headers).text
#解析DOM树结构
html_etree = etree.HTML(r)
names = html_etree.xpath('//*[@class="table table-bordered table-alternate mt-2"]/tbody/tr/td[2]/a/text()')
print (names)
print(len(names),names[0])
#链接
urls = html_etree.xpath('//*[@class="table table-bordered table-alternate mt-2"]/tbody/tr/td[2]/a/@href')
print(urls)
print(len(urls), urls[0])
print("\n")
#-------------------------------------------------------------------------------------------
#获取详细信息
k = 0
while k<len(names):
filename = str(names[k]).strip() + ".txt"
url = "https://attack.mitre.org" + urls[k]
print(url)
#获取正文信息
page = urllib.request.Request(url, headers=headers)
page = urllib.request.urlopen(page)
contents = page.read()
soup = BeautifulSoup(contents, "html.parser")
#获取正文摘要信息
content = ""
for tag in soup.find_all(attrs={"class":"description-body"}):
#contents = tag.find("p").get_text()
contents = tag.find_all("p")
for con in contents:
content += con.get_text().strip() + "###\n" #标记句子结束(第二部分分句用)
#print(content)
#获取表格中的技术信息
for tag in soup.find_all(attrs={"class":"table techniques-used table-bordered mt-2"}):
contents = tag.find("tbody").find_all("tr")
for con in contents:
value = con.find("p").get_text() #存在4列或5列 故获取p值
#print(value)
content += value.strip() + "###\n" #标记句子结束(第二部分分句用)
#删除内容中的参考文献括号 [n]
result = re.sub(u"\\[.*?]", "", content)
print(result)
#文件写入
filename = "Mitre//" + filename
print(filename)
f = open(filename, "w", encoding="utf-8")
f.write(result)
f.close()
k += 1
输出结果如下图所示,共整理100个组织信息。
每个文件显示内容如下图所示:
数据标注采用暴力的方式进行,即定义不同类型的实体名称并利用BIO的方式进行标注。通过ATT&CK技战术方式进行标注,后续可以结合人工校正,同时可以定义更多类型的实体。
- BIO标注
实体名称 | 实体数量 | 示例 |
---|---|---|
APT攻击组织 | 128 | APT32、Lazarus Group |
攻击漏洞 | 56 | CVE-2009-0927 |
区域位置 | 72 | America、Europe |
攻击行业 | 34 | companies、finance |
攻击手法 | 65 | C&C、RAT、DDoS |
利用软件 | 48 | 7-Zip、Microsoft |
操作系统 | 10 | Linux、Windows |
更多标注和预处理请查看上一篇文章。
常见的数据标注工具:
- 图像标注:labelme,LabelImg,Labelbox,RectLabel,CVAT,VIA
- 半自动ocr标注:PPOCRLabel
- NLP标注工具:labelstudio
温馨提示:
由于网站的布局会不断变化和优化,因此读者需要掌握数据采集及语法树定位的基本方法,以不变应万变。此外,读者可以尝试采集所有锻炼甚至是URL跳转链接内容,请读者自行尝试和拓展!
二.数据预处理
假设存在已经采集和标注好的中文数据集,通常采用按字(Char)分隔,如下图所示,古籍为数据集,当然中文威胁情报也类似。
数据集划分为训练集和测试集。
接下来,我们需要读取CSV数据集,并构建汉字词典。关键函数:
- read_csv(filename):读取语料CSV文件
- count_vocab(words,labels):统计不重复词典
- build_vocab():构造词典
完整代码如下:
python
#encoding:utf-8
# By: Eastmount WuShuai 2024-02-05
import re
import os
import csv
import sys
train_data_path = "data/train.csv"
test_data_path = "data/test.csv"
char_vocab_path = "char_vocabs.txt" #字典文件
special_words = ['<PAD>', '<UNK>'] #特殊词表示
final_words = [] #统计词典(不重复出现)
final_labels = [] #统计标记(不重复出现)
#语料文件读取函数
def read_csv(filename):
words = []
labels = []
with open(filename,encoding='utf-8') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
if len(row)>0: #存在空行报错越界
word,label = row[0],row[1]
words.append(word)
labels.append(label)
return words,labels
#统计不重复词典
def count_vocab(words,labels):
fp = open(char_vocab_path, 'a') #注意a为叠加(文件只能运行一次)
k = 0
while k<len(words):
word = words[k]
label = labels[k]
if word not in final_words:
final_words.append(word)
fp.writelines(word + "\n")
if label not in final_labels:
final_labels.append(label)
k += 1
fp.close()
#读取数据并构造原文字典(第一列)
def build_vocab():
words,labels = read_csv(train_data_path)
print(len(words),len(labels),words[:8],labels[:8])
count_vocab(words,labels)
print(len(final_words),len(final_labels))
#测试集
words,labels = read_csv(test_data_path)
print(len(words),len(labels))
count_vocab(words,labels)
print(len(final_words),len(final_labels))
print(final_labels)
#labels生成字典
label_dict = {}
k = 0
for value in final_labels:
label_dict[value] = k
k += 1
print(label_dict)
return label_dict
if __name__ == '__main__':
build_vocab()
输出结果如下,包括训练集数量,并输出前8行文字及标注,以及不重复的汉字个数,以及实体类别14个。
python
['晉', '樂', '王', '鮒', '曰', ':', '', '小']
['S-LOC', 'B-PER', 'I-PER', 'E-PER', 'O', 'O', '', 'O']
xxx 14
输出类别如下。
python
['S-LOC', 'B-PER', 'I-PER', 'E-PER', 'O', '', 'B-LOC',
'E-LOC', 'S-PER', 'S-TIM', 'B-TIM', 'E-TIM', 'I-TIM', 'I-LOC']
接着实体类别进行编码处理,输出结果如下:
python
{'S-LOC': 0, 'B-PER': 1, 'I-PER': 2, 'E-PER': 3, 'O': 4, '': 5, 'B-LOC': 6,
'E-LOC': 7, 'S-PER': 8, 'S-TIM': 9, 'B-TIM': 10, 'E-TIM': 11, 'I-TIM': 12, 'I-LOC': 13}
需要注意:在实体识别中,我们可以通过调用该函数获取识别的实体类别,关键代码如下。然而,由于真实分析中"O"通常建议编码为0,因此建议重新定义字典编码,更方便我们撰写代码,尤其是中文本遇到换句处理时,上述编码会乱序。
python
#原计划
from get_data import build_vocab #调取第一阶段函数
label2idx = build_vocab()
#实际情况
label2idx = {'O': 0,
'S-LOC': 1, 'B-LOC': 2, 'I-LOC': 3, 'E-LOC': 4,
'S-PER': 5, 'B-PER': 6, 'I-PER': 7, 'E-PER': 8,
'S-TIM': 9, 'B-TIM': 10, 'E-TIM': 11, 'I-TIM': 12
}
....
sent_ids = [vocab2idx[char] if char in vocab2idx else vocab2idx['<UNK>'] for char in sent_]
tag_ids = [label2idx[label] if label in label2idx else 0 for label in tag_]
最终生成词典char_vocabs.txt。
三.基于BiLSTM-CRF的实体识别
1.安装keras-contrib
CRF模型作者安装的是 keras-contrib
。
第一步,如果读者直接使用"pip install keras-contrib"可能会报错,远程下载也报错。
- pip install git+https://www.github.com/keras-team/keras-contrib.git
甚至会报错 ModuleNotFoundError: No module named 'keras_contrib'。
第二步,作者从github中下载该资源,并在本地安装。
- https://github.com/keras-team/keras-contrib
- keras-contrib 版本:2.0.8
python
git clone https://www.github.com/keras-team/keras-contrib.git
cd keras-contrib
python setup.py install
安装成功如下图所示:
读者可以从我的资源中下载代码和扩展包。
2.安装Keras
同样需要安装keras和TensorFlow扩展包。
如果TensorFlow下载太慢,可以设置清华大学镜像,实际安装2.2版本。
python
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install tensorflow==2.2
3.中文实体识别
第一步,数据预处理,包括BIO标记及词典转换。
python
#encoding:utf-8
# By: Eastmount WuShuai 2024-02-05
# 参考:https://github.com/huanghao128/zh-nlp-demo
import re
import os
import csv
import sys
from get_data import build_vocab #调取第一阶段函数
#------------------------------------------------------------------------
#第一步 数据预处理
#------------------------------------------------------------------------
train_data_path = "data/train.csv"
test_data_path = "data/test.csv"
val_data_path = "data/val.csv"
char_vocab_path = "char_vocabs.txt" #字典文件(防止多次写入仅读首次生成文件)
special_words = ['<PAD>', '<UNK>'] #特殊词表示
final_words = [] #统计词典(不重复出现)
final_labels = [] #统计标记(不重复出现)
#BIO标记的标签 字母O初始标记为0
#label2idx = build_vocab()
label2idx = {'O': 0,
'S-LOC': 1, 'B-LOC': 2, 'I-LOC': 3, 'E-LOC': 4,
'S-PER': 5, 'B-PER': 6, 'I-PER': 7, 'E-PER': 8,
'S-TIM': 9, 'B-TIM': 10, 'E-TIM': 11, 'I-TIM': 12
}
print(label2idx)
#索引和BIO标签对应
idx2label = {idx: label for label, idx in label2idx.items()}
print(idx2label)
#读取字符词典文件
with open(char_vocab_path, "r") as fo:
char_vocabs = [line.strip() for line in fo]
char_vocabs = special_words + char_vocabs
print(char_vocabs)
#字符和索引编号对应
idx2vocab = {idx: char for idx, char in enumerate(char_vocabs)}
vocab2idx = {char: idx for idx, char in idx2vocab.items()}
print(idx2vocab)
print(vocab2idx)
输出结果如下所示:
python
{'O': 0, 'S-LOC': 1, 'B-LOC': 2, 'I-LOC': 3, 'E-LOC': 4, 'S-PER': 5, 'B-PER': 6,
'I-PER': 7, 'E-PER': 8, 'S-TIM': 9, 'B-TIM': 10, 'E-TIM': 11, 'I-TIM': 12}
{0: 'O', 1: 'S-LOC', 2: 'B-LOC', 3: 'I-LOC', 4: 'E-LOC', 5: 'S-PER', 6: 'B-PER',
7: 'I-PER', 8: 'E-PER', 9: 'S-TIM', 10: 'B-TIM', 11: 'E-TIM', 12: 'I-TIM'}
['<PAD>', '<UNK>', '晉', '樂', '王', '鮒', '曰', ':', '', '小', '旻', ...]
{0: '<PAD>', 1: '<UNK>', 2: '晉', 3: '樂', 4: '王', 5: '鮒', 6: '曰', 7: ':', 8: '', 9: '小', 10: '旻', ... ]
{'<PAD>': 0, '<UNK>': 1, '晉': 2, '樂': 3, '王': 4, '鮒': 5, '曰': 6, ':': 7, '': 8, '小': 9, '旻': 10, ... ]
第二步,读取CSV数据,并获取汉字、标记对应的下标,以下标存储。
python
#------------------------------------------------------------------------
#第二步 数据读取
#------------------------------------------------------------------------
def read_corpus(corpus_path, vocab2idx, label2idx):
datas, labels = [], []
with open(corpus_path, encoding='utf-8') as csvfile:
reader = csv.reader(csvfile)
sent_, tag_ = [], []
for row in reader:
word,label = row[0],row[1]
if word!="" and label!="": #断句
sent_.append(word)
tag_.append(label)
"""
print(sent_) #['晉', '樂', '王', '鮒', '曰', ':']
print(tag_) #['S-LOC', 'B-PER', 'I-PER', 'E-PER', 'O', 'O']
"""
else: #vocab2idx[0] => <PAD>
sent_ids = [vocab2idx[char] if char in vocab2idx else vocab2idx['<UNK>'] for char in sent_]
tag_ids = [label2idx[label] if label in label2idx else 0 for label in tag_]
"""
print(sent_ids,tag_ids)
for idx,idy in zip(sent_ids,tag_ids):
print(idx2vocab[idx],idx2label[idy])
#[2, 3, 4, 5, 6, 7] [1, 6, 7, 8, 0, 0]
#晉 S-LOC 樂 B-PER 王 I-PER 鮒 E-PER 曰 O : O
"""
datas.append(sent_ids) #按句插入列表
labels.append(tag_ids)
sent_, tag_ = [], []
return datas, labels
#原始数据
train_datas_, train_labels_ = read_corpus(train_data_path, vocab2idx, label2idx)
test_datas_, test_labels_ = read_corpus(test_data_path, vocab2idx, label2idx)
#输出测试结果 (第五句语料)
print(len(train_datas_),len(train_labels_),len(test_datas_),len(test_labels_))
print(train_datas_[5])
print([idx2vocab[idx] for idx in train_datas_[5]])
print(train_labels_[5])
print([idx2label[idx] for idx in train_labels_[5]])
输出结果如下,获取汉字和BIO标记的下标。
python
[2, 3, 4, 5, 6, 7] [1, 6, 7, 8, 0, 0]
晉 S-LOC 樂 B-PER 王 I-PER 鮒 E-PER 曰 O : O
其中,第5行数据示例如下:
python
[46, 47, 48, 47, 49, 50, 51, 52, 53, 54, 55, 56]
['齊', '、', '衛', '、', '陳', '大', '夫', '其', '不', '免', '乎', '!']
[1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0]
['S-LOC', 'O', 'S-LOC', 'O', 'S-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
对应语料如下:
第三步,数据填充和one-hot编码。
python
#------------------------------------------------------------------------
#第三步 数据填充 one-hot编码
#------------------------------------------------------------------------
import keras
from keras.preprocessing import sequence
MAX_LEN = 100
VOCAB_SIZE = len(vocab2idx)
CLASS_NUMS = len(label2idx)
#padding data
print('padding sequences')
train_datas = sequence.pad_sequences(train_datas_, maxlen=MAX_LEN)
train_labels = sequence.pad_sequences(train_labels_, maxlen=MAX_LEN)
test_datas = sequence.pad_sequences(test_datas_, maxlen=MAX_LEN)
test_labels = sequence.pad_sequences(test_labels_, maxlen=MAX_LEN)
print('x_train shape:', train_datas.shape)
print('x_test shape:', test_datas.shape)
#encoder one-hot
train_labels = keras.utils.to_categorical(train_labels, CLASS_NUMS)
test_labels = keras.utils.to_categorical(test_labels, CLASS_NUMS)
print('trainlabels shape:', train_labels.shape)
print('testlabels shape:', test_labels.shape)
输出结果如下所示:
python
padding sequences
x_train shape: (xxx, 100)
x_test shape: (xxx, 100)
trainlabels shape: (xxx, 100, 13)
testlabels shape: (xxx, 100, 13)
编码示例如下:
python
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 2163 410 294
980 18]
第四步,构建BiLSTM+CRF模型。
python
#------------------------------------------------------------------------
#第四步 构建BiLSTM+CRF模型
# pip install git+https://www.github.com/keras-team/keras-contrib.git
# 安装过程详见文件夹截图
# ModuleNotFoundError: No module named 'keras_contrib'
#------------------------------------------------------------------------
import numpy as np
from keras.models import Sequential
from keras.models import Model
from keras.layers import Masking, Embedding, Bidirectional, LSTM, \
Dense, Input, TimeDistributed, Activation
from keras_contrib.layers import CRF
from keras_contrib.losses import crf_loss
from keras_contrib.metrics import crf_viterbi_accuracy
from keras import backend as K
from keras.models import load_model
from sklearn import metrics
EPOCHS = 2
EMBED_DIM = 128
HIDDEN_SIZE = 64
MAX_LEN = 100
VOCAB_SIZE = len(vocab2idx)
CLASS_NUMS = len(label2idx)
K.clear_session()
print(VOCAB_SIZE, CLASS_NUMS) #3319 13
#模型构建 BiLSTM-CRF
inputs = Input(shape=(MAX_LEN,), dtype='int32')
x = Masking(mask_value=0)(inputs)
x = Embedding(VOCAB_SIZE, EMBED_DIM, mask_zero=False)(x) #修改掩码False
x = Bidirectional(LSTM(HIDDEN_SIZE, return_sequences=True))(x)
x = TimeDistributed(Dense(CLASS_NUMS))(x)
outputs = CRF(CLASS_NUMS)(x)
model = Model(inputs=inputs, outputs=outputs)
model.summary()
输出结果如下图所示,显示该模型的结构。
第五步,模型训练和测试。flag标记变量分别设置为"train"和"test"。
python
flag = "train"
if flag=="train":
#模型训练
model.compile(loss=crf_loss, optimizer='adam', metrics=[crf_viterbi_accuracy])
model.fit(train_datas, train_labels, epochs=EPOCHS, verbose=1, validation_split=0.1)
score = model.evaluate(test_datas, test_labels, batch_size=256)
print(model.metrics_names)
print(score)
model.save("bilstm_ner_model.h5")
elif flag=="test":
#训练模型
char_vocab_path = "char_vocabs_.txt" #字典文件
model_path = "bilstm_ner_model.h5" #模型文件
ner_labels = label2idx
special_words = ['<PAD>', '<UNK>']
MAX_LEN = 100
#预测结果
model = load_model(model_path, custom_objects={'CRF': CRF}, compile=False)
y_pred = model.predict(test_datas)
y_labels = np.argmax(y_pred, axis=2) #取最大值
z_labels = np.argmax(test_labels, axis=2) #真实值
word_labels = test_datas #真实值
k = 0
final_y = [] #预测结果对应的标签
final_z = [] #真实结果对应的标签
final_word = [] #对应的特征单词
while k<len(y_labels):
y = y_labels[k]
for idx in y:
final_y.append(idx2label[idx])
#print("预测结果:", [idx2label[idx] for idx in y])
z = z_labels[k]
for idx in z:
final_z.append(idx2label[idx])
#print("真实结果:", [idx2label[idx] for idx in z])
word = word_labels[k]
for idx in word:
final_word.append(idx2vocab[idx])
k += 1
print("最终结果大小:", len(final_y),len(final_z))
n = 0
numError = 0
numRight = 0
while n<len(final_y):
if final_y[n]!=final_z[n] and final_z[n]!='O':
numError += 1
if final_y[n]==final_z[n] and final_z[n]!='O':
numRight += 1
n += 1
print("预测错误数量:", numError)
print("预测正确数量:", numRight)
print("Acc:", numRight*1.0/(numError+numRight))
print("预测单词:", [idx2vocab[idx] for idx in test_datas_[5]])
print("真实结果:", [idx2label[idx] for idx in test_labels_[5]])
print("预测结果:", [idx2label[idx] for idx in y_labels[5]][-len(test_datas_[5]):])
训练结果如下所示:
python
Epoch 1/2
32/8439 [..............................] - ETA: 6:51 - loss: 2.5549 - crf_viterbi_accuracy: 3.1250e-04
64/8439 [..............................] - ETA: 3:45 - loss: 2.5242 - crf_viterbi_accuracy: 0.1142
8439/8439 [==============================] - 118s 14ms/step - loss: 0.1833 - crf_viterbi_accuracy: 0.9591 - val_loss: 0.0688 - val_crf_viterbi_accuracy: 0.9820
Epoch 2/10
32/8439 [..............................] - ETA: 19s - loss: 0.0644 - crf_viterbi_accuracy: 0.9825
64/8439 [..............................] - ETA: 42s - loss: 0.0592 - crf_viterbi_accuracy: 0.9845
...
['loss', 'crf_viterbi_accuracy']
[0.043232945389307574, 0.9868513941764832]
最终测试结果如下所示,由于作者数据集仅放了少量数据,且未进行调参比较,真实数据更多且效果会更好。
python
预测错误数量: 2183
预测正确数量: 2209
Acc: 0.5029599271402551
预测单词: ['冬', ',', '楚', '公', '子', '罷', '如', '晉', '聘', ',', '且', '涖', '盟', '。']
真实结果: ['O', 'O', 'B-PER', 'I-PER', 'I-PER', 'E-PER', 'O', 'S-LOC', 'O', 'O', 'O', 'O', 'O', 'O']
预测结果: ['O', 'O', 'B-PER', 'E-PER', 'E-PER', 'E-PER', 'O', 'S-LOC', 'O', 'O', 'O', 'O', 'O', 'O']
四.基于BiGRU-CRF的实体识别
接下来构建BiGRU-CRF代码,以完整代码为例,并将预测结果存储在CSV文件上。
python
#encoding:utf-8
# By: Eastmount WuShuai 2024-02-05
import re
import os
import csv
import sys
from get_data import build_vocab #调取第一阶段函数
#------------------------------------------------------------------------
#第一步 数据预处理
#------------------------------------------------------------------------
train_data_path = "data/train.csv"
test_data_path = "data/test.csv"
val_data_path = "data/val.csv"
char_vocab_path = "char_vocabs.txt" #字典文件(防止多次写入仅读首次生成文件)
special_words = ['<PAD>', '<UNK>'] #特殊词表示
final_words = [] #统计词典(不重复出现)
final_labels = [] #统计标记(不重复出现)
#BIO标记的标签 字母O初始标记为0
#label2idx = build_vocab()
label2idx = {'O': 0,
'S-LOC': 1, 'B-LOC': 2, 'I-LOC': 3, 'E-LOC': 4,
'S-PER': 5, 'B-PER': 6, 'I-PER': 7, 'E-PER': 8,
'S-TIM': 9, 'B-TIM': 10, 'E-TIM': 11, 'I-TIM': 12
}
#索引和BIO标签对应
idx2label = {idx: label for label, idx in label2idx.items()}
#读取字符词典文件
with open(char_vocab_path, "r") as fo:
char_vocabs = [line.strip() for line in fo]
char_vocabs = special_words + char_vocabs
#字符和索引编号对应
idx2vocab = {idx: char for idx, char in enumerate(char_vocabs)}
vocab2idx = {char: idx for idx, char in idx2vocab.items()}
#------------------------------------------------------------------------
#第二步 数据读取
#------------------------------------------------------------------------
def read_corpus(corpus_path, vocab2idx, label2idx):
datas, labels = [], []
with open(corpus_path, encoding='utf-8') as csvfile:
reader = csv.reader(csvfile)
sent_, tag_ = [], []
for row in reader:
word,label = row[0],row[1]
if word!="" and label!="": #断句
sent_.append(word)
tag_.append(label)
else: #vocab2idx[0] => <PAD>
sent_ids = [vocab2idx[char] if char in vocab2idx else vocab2idx['<UNK>'] for char in sent_]
tag_ids = [label2idx[label] if label in label2idx else 0 for label in tag_]
datas.append(sent_ids) #按句插入列表
labels.append(tag_ids)
sent_, tag_ = [], []
return datas, labels
#原始数据
train_datas_, train_labels_ = read_corpus(train_data_path, vocab2idx, label2idx)
test_datas_, test_labels_ = read_corpus(test_data_path, vocab2idx, label2idx)
#------------------------------------------------------------------------
#第三步 数据填充 one-hot编码
#------------------------------------------------------------------------
import keras
from keras.preprocessing import sequence
MAX_LEN = 100
VOCAB_SIZE = len(vocab2idx)
CLASS_NUMS = len(label2idx)
#padding data
print('padding sequences')
train_datas = sequence.pad_sequences(train_datas_, maxlen=MAX_LEN)
train_labels = sequence.pad_sequences(train_labels_, maxlen=MAX_LEN)
test_datas = sequence.pad_sequences(test_datas_, maxlen=MAX_LEN)
test_labels = sequence.pad_sequences(test_labels_, maxlen=MAX_LEN)
#encoder one-hot
train_labels = keras.utils.to_categorical(train_labels, CLASS_NUMS)
test_labels = keras.utils.to_categorical(test_labels, CLASS_NUMS)
#------------------------------------------------------------------------
#第四步 构建BiGRU+CRF模型
#------------------------------------------------------------------------
import numpy as np
from keras.models import Sequential
from keras.models import Model
from keras.layers import Masking, Embedding, Bidirectional, LSTM, GRU, \
Dense, Input, TimeDistributed, Activation
from keras_contrib.layers import CRF
from keras_contrib.losses import crf_loss
from keras_contrib.metrics import crf_viterbi_accuracy
from keras import backend as K
from keras.models import load_model
from sklearn import metrics
EPOCHS = 2
EMBED_DIM = 128
HIDDEN_SIZE = 64
MAX_LEN = 100
VOCAB_SIZE = len(vocab2idx)
CLASS_NUMS = len(label2idx)
K.clear_session()
print(VOCAB_SIZE, CLASS_NUMS)
#模型构建 BiGRU-CRF
inputs = Input(shape=(MAX_LEN,), dtype='int32')
x = Masking(mask_value=0)(inputs)
x = Embedding(VOCAB_SIZE, EMBED_DIM, mask_zero=False)(x) #修改掩码False
x = Bidirectional(GRU(HIDDEN_SIZE, return_sequences=True))(x)
x = TimeDistributed(Dense(CLASS_NUMS))(x)
outputs = CRF(CLASS_NUMS)(x)
model = Model(inputs=inputs, outputs=outputs)
model.summary()
flag = "test"
if flag=="train":
#模型训练
model.compile(loss=crf_loss, optimizer='adam', metrics=[crf_viterbi_accuracy])
model.fit(train_datas, train_labels, epochs=EPOCHS, verbose=1, validation_split=0.1)
score = model.evaluate(test_datas, test_labels, batch_size=256)
print(model.metrics_names)
print(score)
model.save("bigru_ner_model.h5")
elif flag=="test":
#训练模型
char_vocab_path = "char_vocabs_.txt" #字典文件
model_path = "bigru_ner_model.h5" #模型文件
ner_labels = label2idx
special_words = ['<PAD>', '<UNK>']
MAX_LEN = 100
#预测结果
model = load_model(model_path, custom_objects={'CRF': CRF}, compile=False)
y_pred = model.predict(test_datas)
y_labels = np.argmax(y_pred, axis=2) #取最大值
z_labels = np.argmax(test_labels, axis=2) #真实值
word_labels = test_datas #真实值
k = 0
final_y = [] #预测结果对应的标签
final_z = [] #真实结果对应的标签
final_word = [] #对应的特征单词
while k<len(y_labels):
y = y_labels[k]
for idx in y:
final_y.append(idx2label[idx])
z = z_labels[k]
for idx in z:
final_z.append(idx2label[idx])
word = word_labels[k]
for idx in word:
final_word.append(idx2vocab[idx])
k += 1
n = 0
numError = 0
numRight = 0
while n<len(final_y):
if final_y[n]!=final_z[n] and final_z[n]!='O':
numError += 1
if final_y[n]==final_z[n] and final_z[n]!='O':
numRight += 1
n += 1
print("预测错误数量:", numError)
print("预测正确数量:", numRight)
print("Acc:", numRight*1.0/(numError+numRight))
print("预测单词:", [idx2vocab[idx] for idx in test_datas_[5]])
print("真实结果:", [idx2label[idx] for idx in test_labels_[5]])
print("预测结果:", [idx2label[idx] for idx in y_labels[5]][-len(test_datas_[5]):])
#文件存储
fw = open("Final_BiGRU_CRF_Result.csv", "w", encoding="utf8", newline='')
fwrite = csv.writer(fw)
fwrite.writerow(['pre_label','real_label', 'word'])
n = 0
while n<len(final_y):
fwrite.writerow([final_y[n],final_z[n],final_word[n]])
n += 1
fw.close()
输出结果如下所示:
python
['loss', 'crf_viterbi_accuracy']
[0.03543611364953834, 0.9894005656242371]
生成文件如下图所示:
五.总结
写到这里这篇文章就结束,希望对您有所帮助,后续将结合经典的Bert进行分享。忙碌的2024,真的很忙,项目本子论文毕业工作,等忙完后好好写几篇安全博客,感谢支持和陪伴,尤其是家人的鼓励和支持, 继续加油!
- 一.ATT&CK数据采集
- 二.数据预处理
- 三.基于BiLSTM-CRF的实体识别
1.安装keras-contrib
2.安装Keras
3.中文实体识别 - 四.基于BiGRU-CRF的实体识别
- 五.总结
人生路是一个个十字路口,一次次博弈,一次次纠结和得失组成。得失得失,有得有失,不同的选择,不一样的精彩。虽然累和忙,但看到小珞珞还是挺满足的,感谢家人的陪伴。望小珞能开心健康成长,爱你们喔,继续干活,加油!
(By:Eastmount 2024-02-07 夜于贵阳 http://blog.csdn.net/eastmount/ )