142 lines
4.8 KiB
Python
142 lines
4.8 KiB
Python
# coding=utf-8
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import fileinput
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import warnings
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import os
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import tensorflow as tf
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import sys
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import re
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import nltk
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import sklearn
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import tensorflow.keras as keras
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import tensorflow.keras.preprocessing as keras_preprocessing
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from sklearn.preprocessing import StandardScaler
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import chardet
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import math
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from joblib import load
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g_word_dict = {}
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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os.environ["CUDA_VISIBLE_DEVICES"] = '-1'
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def os_listdir_ex(file_dir, find_name): # 祖传代码
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result = []
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for root, dirs, files in os.walk(file_dir):
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for file in files:
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if os.path.splitext(file)[1] == find_name:
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result.append(os.path.join(root, file))
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# return result # 测试用
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return result
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def get_file_length(pFile): # 得到文件长度,祖传代码
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fsize = os.path.getsize(pFile)
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return int(fsize)
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def flush_file(pFile): # 清洗php注释
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file = open(pFile, 'r', encoding='gb18030', errors='ignore')
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read_string = file.read()
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file.close()
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m = re.compile(r'/\*.*?\*/', re.S)
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result = re.sub(m, '', read_string)
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m = re.compile(r'//.*')
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result = re.sub(m, '', result)
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m = re.compile(r'#.*')
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result = re.sub(m, '', result)
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return result
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# 得到文件熵 https://blog.csdn.net/jliang3/article/details/88359063
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def get_file_entropy(pFile):
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clean_string = flush_file(pFile)
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text_list = {}
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_sum = 0
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result = 0
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for word_iter in clean_string:
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if word_iter != '\n' and word_iter != ' ':
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if word_iter not in text_list.keys():
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text_list[word_iter] = 1
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else:
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text_list[word_iter] = text_list[word_iter] + 1
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for index in text_list.keys():
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_sum = _sum + text_list[index]
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for index in text_list.keys():
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result = result - float(text_list[index])/_sum * \
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math.log(float(text_list[index])/_sum, 2)
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return result
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def vectorize_sequences(sequences, dimention=1337):
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# 创建一个大小为（25000，10000）的全零矩阵
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results = np.zeros((len(sequences), dimention))
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for i, sequence in enumerate(sequences):
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if i > dimention:
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break
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try:
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results[i, sequence] = 1.
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except:
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break
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return results
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def get_file_word_bag(pFile):
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global g_word_dict
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english_punctuations = [',', '.', ':', ';', '?',
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'(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%', 'php', '<', '>', '\'']
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clean_string = flush_file(pFile)
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word_list = nltk.word_tokenize(clean_string)
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# 过滤掉不干净的
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word_list = [
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word_iter for word_iter in word_list if word_iter not in english_punctuations]
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keras_token = keras.preprocessing.text.Tokenizer() # 初始化标注器
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keras_token.fit_on_texts(word_list) # 学习出文本的字典
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g_word_dict.update(keras_token.word_index)
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# 通过texts_to_sequences 这个dict可以将每个string的每个词转成数字
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sequences_data = keras_token.texts_to_sequences(word_list)
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# 将每条文本的长度设置一个固定值, ps 超过1337个字符的"单词"不用说肯定是某个骇客想把大马变免杀马
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# word_bag = keras_preprocessing.sequence.pad_sequences(sequences_data, maxlen=1337, dtype='int16')
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word_bag = []
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for index in range(0, len(sequences_data)):
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if len(sequences_data[index]) != 0:
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for zeus in range(0, len(sequences_data[index])):
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word_bag.append(sequences_data[index][zeus])
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return word_bag
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file_path = '.\\1.php'
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entropy = get_file_entropy(file_path)
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length = get_file_length(file_path)
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word_bag = get_file_word_bag(file_path)
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array_input = np.array([[entropy, length]])
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data_frame = pd.DataFrame(
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{'length': [length], 'entropy': [entropy], 'word_bag': [word_bag]}, columns=['length', 'entropy', 'word_bag'])
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# scaler = StandardScaler()
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scaler_entropy = load('scaler_entropy.joblib')
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scaler_length = load('scaler_length.joblib')
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data_frame['length_scaled'] = scaler_length.transform(
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data_frame['length'].values.reshape(-1, 1))
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data_frame['entropy_scaled'] = scaler_entropy.transform(
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data_frame['entropy'].values.reshape(-1, 1))
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data_train_pre = data_frame.filter(items=['length_scaled', 'entropy_scaled'])
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# data_train_pre = data_frame.filter(items=['length', 'entropy'])
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data_train_x_1 = tf.constant(data_train_pre)
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data_train_x_2 = tf.constant(
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vectorize_sequences(data_frame['word_bag'].values))
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print(data_frame.head())
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model_name = 'huoji1.h5' # huoji.h5 huoji_scaled.h5 huoji_no_scale.h5
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model = keras.models.load_model(model_name)
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model.summary()
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print(data_train_x_1, data_train_x_2)
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prediction = model.predict([data_train_x_1, data_train_x_2])
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print(prediction)
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