[系统安全] 五十.恶意家族分类 (2)基于API序列和深度学习的恶意家族分类实例详解
VSole2023-06-28 09:26:22
一.恶意软件分析
恶意软件或恶意代码分析通常包括静态分析和动态分析。特征种类如果按照恶意代码是否在用户环境或仿真环境中运行，可以划分为静态特征和动态特征。
那么，如何提取恶意软件的静态特征或动态特征呢？ 因此，第一部分将简要介绍静态特征和动态特征。
1.静态特征
没有真实运行的特征，通常包括：
- 字节码
- 二进制代码转换成了字节码，比较原始的一种特征，没有进行任何处理
- IAT表
- PE结构中比较重要的部分，声明了一些函数及所在位置，便于程序执行时导入，表和功能比较相关
- Android权限表
- 如果你的APP声明了一些功能用不到的权限，可能存在恶意目的，如手机信息
- 可打印字符
- 将二进制代码转换为ASCII码，进行相关统计
- IDA反汇编跳转块
- IDA工具调试时的跳转块，对其进行处理作为序列数据或图数据
- 常用API函数
- 恶意软件图像化
静态特征提取方式：
- CAPA
- – https://github.com/mandiant/capa
- IDA Pro
- 安全厂商沙箱
2.动态特征
相当于静态特征更耗时，它要真正去执行代码。通常包括：
– API调用关系：比较明显的特征，调用了哪些API，表述对应的功能
– 控制流图：软件工程中比较常用，机器学习将其表示成向量，从而进行分类
– 数据流图：软件工程中比较常用，机器学习将其表示成向量，从而进行分类
动态特征提取方式：
- Cuckoo
- – https://github.com/cuckoosandbox/cuckoo
- CAPE
- – https://github.com/kevoreilly/CAPEv2
- – https://capev2.readthedocs.io/en/latest/
- 安全厂商沙箱
二.基于CNN的恶意家族检测
前面的系列文章详细介绍如何提取恶意软件的静态和动态特征，包括API序列。接下来将构建深度学习模型学习API序列实现分类。基本流程如下：
1.数据集
整个数据集包括5类恶意家族的样本，每个样本经过先前的CAPE工具成功提取的动态API序列。数据集分布情况如下所示：（建议读者提取自己数据集的样本，包括BIG2015、BODMAS等）
恶意家族类别数量训练集测试集AAAAclass1352242110BBBBclass2335235100CCCCclass3363243120DDDDclass4293163130EEEEclass5548358190 数据集分为训练集和测试集，如下图所示：
数据集中主要包括四个字段，即序号、恶意家族类别、Md5值、API序列或特征。
需要注意，在特征提取过程中涉及大量数据预处理和清洗的工作，读者需要结合实际需求完成。比如提取特征为空值的过滤代码。
#coding:utf-8#By:Eastmount CSDN 2023-05-31import csvimport reimport os csv.field_size_limit(500 * 1024 * 1024)filename = "AAAA_result.csv"writename = "AAAA_result_final.csv"fw = open(writename, mode="w", newline="")writer = csv.writer(fw)writer.writerow(['no', 'type', 'md5', 'api'])with open(filename,encoding='utf-8') as fr: reader = csv.reader(fr) no = 1 for row in reader: #['no','type','md5','api'] tt = row[1] md5 = row[2] api = row[3] #print(no,tt,md5,api) #api空值的过滤 if api=="" or api=="api": continue else: writer.writerow([str(no),tt,md5,api]) no += 1fr.close()
2.模型构建
该模型的基本步骤如下：
第一步 数据读取第二步 OneHotEncoder()编码第三步 使用Tokenizer对词组进行编码第四步 建立CNN模型并训练第五步 预测及评估第六步 验证算法
构建模型如下图所示：
完整代码如下所示：
# -*- coding: utf-8 -*-# By:Eastmount CSDN 2023-06-27import pickleimport pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn import metricsimport tensorflow as tffrom sklearn.preprocessing import LabelEncoder,OneHotEncoderfrom keras.models import Modelfrom keras.layers import LSTM, Activation, Dense, Dropout, Input, Embeddingfrom keras.layers import Convolution1D, MaxPool1D, Flattenfrom keras.optimizers import RMSpropfrom keras.layers import Bidirectionalfrom keras.preprocessing.text import Tokenizerfrom keras.preprocessing import sequencefrom keras.callbacks import EarlyStoppingfrom keras.models import load_modelfrom keras.models import Sequentialfrom keras.layers.merge import concatenateimport time
"""import osos.environ["CUDA_DEVICES_ORDER"] = "PCI_BUS_IS"os.environ["CUDA_VISIBLE_DEVICES"] = "0"gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))"""
start = time.clock()
#---------------------------------------第一步 数据读取------------------------------------# 读取测数据集train_df = pd.read_csv("..\\train_dataset.csv")val_df = pd.read_csv("..\\val_dataset.csv")test_df = pd.read_csv("..\\test_dataset.csv")
# 指定数据类型 否则AttributeError: 'float' object has no attribute 'lower' 存在文本为空的现象# train_df.SentimentText = train_df.SentimentText.astype(str)print(train_df.head())
# 解决中文显示问题plt.rcParams['font.sans-serif'] = ['KaiTi'] #指定默认字体 SimHei黑体plt.rcParams['axes.unicode_minus'] = False #解决保存图像是负号'
#---------------------------------第二步 OneHotEncoder()编码---------------------------------# 对数据集的标签数据进行编码 (no apt md5 api)train_y = train_df.aptprint("Label:")print(train_y[:10])val_y = val_df.apttest_y = test_df.aptle = LabelEncoder()train_y = le.fit_transform(train_y).reshape(-1,1)print("LabelEncoder")print(train_y[:10])print(len(train_y))val_y = le.transform(val_y).reshape(-1,1)test_y = le.transform(test_y).reshape(-1,1)Labname = le.classes_print(Labname)
# 对数据集的标签数据进行one-hot编码ohe = OneHotEncoder()train_y = ohe.fit_transform(train_y).toarray()val_y = ohe.transform(val_y).toarray()test_y = ohe.transform(test_y).toarray()print("OneHotEncoder:")print(train_y[:10])
#-------------------------------第三步 使用Tokenizer对词组进行编码-------------------------------# 使用Tokenizer对词组进行编码# 当我们创建了一个Tokenizer对象后，使用该对象的fit_on_texts()函数，以空格去识别每个词# 可以将输入的文本中的每个词编号，编号是根据词频的，词频越大，编号越小max_words = 1000max_len = 200tok = Tokenizer(num_words=max_words) #使用的最大词语数为1000print(train_df.api[:5])print(type(train_df.api))
# 提取token：apitrain_value = train_df.apitrain_content = [str(a) for a in train_value.tolist()]val_value = val_df.apival_content = [str(a) for a in val_value.tolist()]test_value = test_df.apitest_content = [str(a) for a in test_value.tolist()]tok.fit_on_texts(train_content)print(tok)
# 保存训练好的Tokenizer和导入# savingwith open('tok.pickle', 'wb') as handle: pickle.dump(tok, handle, protocol=pickle.HIGHEST_PROTOCOL)# loadingwith open('tok.pickle', 'rb') as handle: tok = pickle.load(handle)
# 使用word_index属性可以看到每次词对应的编码# 使用word_counts属性可以看到每个词对应的频数for ii,iterm in enumerate(tok.word_index.items()): if ii < 10: print(iterm) else: breakprint("===================") for ii,iterm in enumerate(tok.word_counts.items()): if ii < 10: print(iterm) else: break
# 使用tok.texts_to_sequences()将数据转化为序列# 使用sequence.pad_sequences()将每个序列调整为相同的长度# 对每个词编码之后，每句新闻中的每个词就可以用对应的编码表示，即每条新闻可以转变成一个向量了train_seq = tok.texts_to_sequences(train_content)val_seq = tok.texts_to_sequences(val_content)test_seq = tok.texts_to_sequences(test_content)
# 将每个序列调整为相同的长度train_seq_mat = sequence.pad_sequences(train_seq,maxlen=max_len)val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len)test_seq_mat = sequence.pad_sequences(test_seq,maxlen=max_len)print(train_seq_mat.shape) #(1241, 200)print(val_seq_mat.shape) #(459, 200)print(test_seq_mat.shape) #(650, 200)print(train_seq_mat[:2])
#-------------------------------第四步 建立CNN模型并训练-------------------------------num_labels = 5inputs = Input(name='inputs',shape=[max_len], dtype='float64')
# 词嵌入（使用预训练的词向量）layer = Embedding(max_words+1, 256, input_length=max_len, trainable=False)(inputs)
# 词窗大小分别为3,4,5cnn = Convolution1D(256, 3, padding='same', strides = 1, activation='relu')(layer)cnn = MaxPool1D(pool_size=3)(cnn)
# 合并三个模型的输出向量flat = Flatten()(cnn) drop = Dropout(0.4)(flat)main_output = Dense(num_labels, activation='softmax')(drop)model = Model(inputs=inputs, outputs=main_output)model.summary()model.compile(loss="categorical_crossentropy", optimizer='adam', #RMSprop() metrics=["accuracy"])
# 增加判断 防止再次训练flag = "train"if flag == "train": print("模型训练") # 模型训练 model_fit = model.fit(train_seq_mat, train_y, batch_size=64, epochs=15, validation_data=(val_seq_mat,val_y), callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.001)] #当val-loss不再提升时停止训练 0.0001 ) # 保存模型 model.save('cnn_model.h5') del model # deletes the existing model # 计算时间 elapsed = (time.clock() - start) print("Time used:", elapsed) print(model_fit.history) else: print("模型预测") # 导入已经训练好的模型 model = load_model('cnn_model.h5') #--------------------------------------第五步 预测及评估-------------------------------- # 对测试集进行预测 test_pre = model.predict(test_seq_mat) # 评价预测效果，计算混淆矩阵 confm = metrics.confusion_matrix(np.argmax(test_y,axis=1), np.argmax(test_pre,axis=1)) print(confm) print(metrics.classification_report(np.argmax(test_y,axis=1), np.argmax(test_pre,axis=1), digits=4)) print("accuracy", metrics.accuracy_score(np.argmax(test_y, axis=1), np.argmax(test_pre, axis=1))) # 结果存储 f1 = open("cnn_test_pre.txt", "w") for n in np.argmax(test_pre, axis=1): f1.write(str(n) + "\n") f1.close()
f2 = open("cnn_test_y.txt", "w") for n in np.argmax(test_y, axis=1): f2.write(str(n) + "\n") f2.close()
plt.figure(figsize=(8,8)) sns.heatmap(confm.T, square=True, annot=True, fmt='d', cbar=False, linewidths=.6, cmap="YlGnBu") plt.xlabel('True label',size = 14) plt.ylabel('Predicted label', size = 14) plt.xticks(np.arange(5)+0.5, Labname, size = 12) plt.yticks(np.arange(5)+0.5, Labname, size = 12) plt.savefig('cnn_result.png') plt.show()
#--------------------------------------第六步 验证算法-------------------------------- # 使用tok对验证数据集重新预处理 val_seq = tok.texts_to_sequences(val_content) # 将每个序列调整为相同的长度 val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len) # 对验证集进行预测 val_pre = model.predict(val_seq_mat) print(metrics.classification_report(np.argmax(val_y,axis=1), np.argmax(val_pre,axis=1), digits=4)) print("accuracy", metrics.accuracy_score(np.argmax(val_y, axis=1), np.argmax(val_pre, axis=1))) # 计算时间 elapsed = (time.clock() - start) print("Time used:", elapsed)
3.实验结果
最终运行结果及其生成文件如下图所示：
输出中间过程结果如下所示：
no ... api0 1 ... GetSystemInfo;HeapCreate;NtAllocateVirtualMemo...1 2 ... GetSystemInfo;HeapCreate;NtAllocateVirtualMemo...2 3 ... NtQueryValueKey;GetSystemTimeAsFileTime;HeapCr...3 4 ... NtQueryValueKey;NtClose;NtAllocateVirtualMemor...4 5 ... NtOpenFile;NtCreateSection;NtMapViewOfSection;...
[5 rows x 4 columns]Label:0 class11 class12 class13 class14 class15 class16 class17 class18 class19 class1Name: apt, dtype: objectLabelEncoder[[0] [0] [0] [0] [0] [0] [0] [0] [0] [0]]1241['class1' 'class2' 'class3' 'class4' 'class5']OneHotEncoder:[[1. 0. 0. 0. 0.] [1. 0. 0. 0. 0.] [1. 0. 0. 0. 0.] [1. 0. 0. 0. 0.] [1. 0. 0. 0. 0.] [1. 0. 0. 0. 0.] [1. 0. 0. 0. 0.] [1. 0. 0. 0. 0.] [1. 0. 0. 0. 0.] [1. 0. 0. 0. 0.]]0 GetSystemInfo;HeapCreate;NtAllocateVirtualMemo...1 GetSystemInfo;HeapCreate;NtAllocateVirtualMemo...2 NtQueryValueKey;GetSystemTimeAsFileTime;HeapCr...3 NtQueryValueKey;NtClose;NtAllocateVirtualMemor...4 NtOpenFile;NtCreateSection;NtMapViewOfSection;...Name: api, dtype: object<class 'pandas.core.series.Series'><keras_preprocessing.text.Tokenizer object at 0x0000028E55D36B08>
('regqueryvalueexw', 1)('ntclose', 2)('ldrgetprocedureaddress', 3)('regopenkeyexw', 4)('regclosekey', 5)('ntallocatevirtualmemory', 6)('sendmessagew', 7)('ntwritefile', 8)('process32nextw', 9)('ntdeviceiocontrolfile', 10)===================('getsysteminfo', 2651)('heapcreate', 2996)('ntallocatevirtualmemory', 115547)('ntqueryvaluekey', 24120)('getsystemtimeasfiletime', 52727)('ldrgetdllhandle', 25135)('ldrgetprocedureaddress', 199952)('memcpy', 9008)('setunhandledexceptionfilter', 1504)('ntcreatefile', 43260)
(1241, 200)(459, 200)(650, 200)[[ 3 135 3 3 2 21 3 3 4 3 96 3 3 4 96 4 96 20 22 20 3 6 6 23 128 129 3 103 23 56 2 103 23 20 3 23 3 3 3 3 4 1 5 23 12 131 12 20 3 10 2 10 2 20 3 4 5 27 3 10 2 6 10 2 3 10 2 10 2 3 10 2 10 2 10 2 10 2 10 2 3 10 2 10 2 10 2 10 2 3 3 3 36 4 3 23 20 3 5 207 34 6 6 6 11 11 6 11 6 6 6 6 6 6 6 6 6 11 6 6 11 6 11 6 11 6 6 11 6 34 3 141 3 140 3 3 141 34 6 2 21 4 96 4 96 4 96 23 3 3 12 131 12 10 2 10 2 4 5 27 10 2 6 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2 36 4 23 5 207 6 3 3 12 131 12 132 3] [ 27 4 27 4 27 4 27 4 27 27 5 27 4 27 4 27 27 27 27 27 27 27 5 27 4 27 4 27 4 27 4 27 4 27 4 27 4 27 4 27 4 27 5 52 2 21 4 5 1 1 1 5 21 25 2 52 12 33 51 28 34 30 2 52 2 21 4 5 27 5 52 6 6 52 4 1 5 4 52 54 7 7 20 52 7 52 7 7 6 4 4 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 5 5 3 7 50 50 50 95 50 50 50 50 50 4 1 5 4 3 3 3 3 3 7 7 7 3 7 3 7 3 60 3 3 7 7 7 7 60 3 7 7 7 7 7 7 7 7 52 20 3 3 3 14 14 60 18 19 18 19 2 21 4 5 18 19 18 19 18 19 18 19 7 7 7 7 7 7 7 7 7 7 7 52 7 7 7 7 7 60 7 7 7 7]]
模型训练过程如下：
模型训练Epoch 1/15
1/20 [>.............................] - ETA: 5s - loss: 1.5986 - accuracy: 0.2656 2/20 [==>...........................] - ETA: 1s - loss: 1.6050 - accuracy: 0.2266 3/20 [===>..........................] - ETA: 1s - loss: 1.5777 - accuracy: 0.2292 4/20 [=====>........................] - ETA: 2s - loss: 1.5701 - accuracy: 0.2500 5/20 [======>.......................] - ETA: 2s - loss: 1.5628 - accuracy: 0.2719 6/20 [========>.....................] - ETA: 3s - loss: 1.5439 - accuracy: 0.3125 7/20 [=========>....................] - ETA: 3s - loss: 1.5306 - accuracy: 0.3348 8/20 [===========>..................] - ETA: 3s - loss: 1.5162 - accuracy: 0.3535 9/20 [============>.................] - ETA: 3s - loss: 1.5020 - accuracy: 0.369810/20 [==============>...............] - ETA: 3s - loss: 1.4827 - accuracy: 0.396911/20 [===============>..............] - ETA: 3s - loss: 1.4759 - accuracy: 0.402012/20 [=================>............] - ETA: 3s - loss: 1.4734 - accuracy: 0.403613/20 [==================>...........] - ETA: 3s - loss: 1.4456 - accuracy: 0.425514/20 [====================>.........] - ETA: 3s - loss: 1.4322 - accuracy: 0.435315/20 [=====================>........] - ETA: 2s - loss: 1.4157 - accuracy: 0.446916/20 [=======================>......] - ETA: 2s - loss: 1.4093 - accuracy: 0.448217/20 [========================>.....] - ETA: 2s - loss: 1.4010 - accuracy: 0.453118/20 [==========================>...] - ETA: 1s - loss: 1.3920 - accuracy: 0.460119/20 [===========================>..] - ETA: 0s - loss: 1.3841 - accuracy: 0.463820/20 [==============================] - ETA: 0s - loss: 1.3763 - accuracy: 0.467420/20 [==============================] - 20s 1s/step - loss: 1.3763 - accuracy: 0.4674 - val_loss: 1.3056 - val_accuracy: 0.4837
Time used: 26.1328806{'loss': [1.3762551546096802], 'accuracy': [0.467365026473999], 'val_loss': [1.305567979812622], 'val_accuracy': [0.48366013169288635]}
最终预测结果如下所示：
模型测[[ 40 14 11 1 44] [ 16 57 10 0 17] [ 6 30 61 0 23] [ 12 20 15 47 36] [ 11 14 19 0 146]] precision recall f1-score support
0 0.4706 0.3636 0.4103 110 1 0.4222 0.5700 0.4851 100 2 0.5259 0.5083 0.5169 120 3 0.9792 0.3615 0.5281 130 4 0.5489 0.7684 0.6404 190
accuracy 0.5400 650 macro avg 0.5893 0.5144 0.5162 650weighted avg 0.5980 0.5400 0.5323 650
accuracy 0.54
precision recall f1-score support
0 0.9086 0.4517 0.6034 352 1 0.5943 0.5888 0.5915 107 2 0.0000 0.0000 0.0000 0 3 0.0000 0.0000 0.0000 0 4 0.0000 0.0000 0.0000 0
accuracy 0.4837 459 macro avg 0.3006 0.2081 0.2390 459weighted avg 0.8353 0.4837 0.6006 459
accuracy 0.48366013071895425Time used: 14.170902800000002
思考：
然而，整个预测结果效果较差，请读者思考，这是为什么呢？我们能不能通过调参进行优化，又如何改进算法呢？本文仅提供基本思路和代码，更多优化及完善需要读者学会独立解决，加油喔！
三.基于BiLSTM的恶意家族检测
1.模型构建
该模型的基本步骤如下：
第一步 数据读取第二步 OneHotEncoder()编码第三步 使用Tokenizer对词组进行编码第四步 建立BiLSTM模型并训练第五步 预测及评估第六步 验证算法
构建模型如下图所示：
完整代码如下所示：
# -*- coding: utf-8 -*-# By:Eastmount CSDN 2023-06-27import pickleimport pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn import metricsimport tensorflow as tffrom sklearn.preprocessing import LabelEncoder,OneHotEncoderfrom keras.models import Modelfrom keras.layers import LSTM, Activation, Dense, Dropout, Input, Embeddingfrom keras.layers import Convolution1D, MaxPool1D, Flattenfrom keras.optimizers import RMSpropfrom keras.layers import Bidirectionalfrom keras.preprocessing.text import Tokenizerfrom keras.preprocessing import sequencefrom keras.callbacks import EarlyStoppingfrom keras.models import load_modelfrom keras.models import Sequentialfrom keras.layers.merge import concatenateimport time
start = time.clock()
#---------------------------------------第一步 数据读取------------------------------------# 读取测数据集train_df = pd.read_csv("..\\train_dataset.csv")val_df = pd.read_csv("..\\val_dataset.csv")test_df = pd.read_csv("..\\test_dataset.csv")print(train_df.head())
# 解决中文显示问题plt.rcParams['font.sans-serif'] = ['KaiTi']plt.rcParams['axes.unicode_minus'] = False
#---------------------------------第二步 OneHotEncoder()编码---------------------------------# 对数据集的标签数据进行编码 (no apt md5 api)train_y = train_df.aptval_y = val_df.apttest_y = test_df.aptle = LabelEncoder()train_y = le.fit_transform(train_y).reshape(-1,1)val_y = le.transform(val_y).reshape(-1,1)test_y = le.transform(test_y).reshape(-1,1)Labname = le.classes_
# 对数据集的标签数据进行one-hot编码ohe = OneHotEncoder()train_y = ohe.fit_transform(train_y).toarray()val_y = ohe.transform(val_y).toarray()test_y = ohe.transform(test_y).toarray()
#-------------------------------第三步 使用Tokenizer对词组进行编码-------------------------------# 使用Tokenizer对词组进行编码max_words = 2000max_len = 300tok = Tokenizer(num_words=max_words)
# 提取token：apitrain_value = train_df.apitrain_content = [str(a) for a in train_value.tolist()]val_value = val_df.apival_content = [str(a) for a in val_value.tolist()]test_value = test_df.apitest_content = [str(a) for a in test_value.tolist()]tok.fit_on_texts(train_content)print(tok)
# 保存训练好的Tokenizer和导入with open('tok.pickle', 'wb') as handle: pickle.dump(tok, handle, protocol=pickle.HIGHEST_PROTOCOL)with open('tok.pickle', 'rb') as handle: tok = pickle.load(handle)
# 使用tok.texts_to_sequences()将数据转化为序列train_seq = tok.texts_to_sequences(train_content)val_seq = tok.texts_to_sequences(val_content)test_seq = tok.texts_to_sequences(test_content)
# 将每个序列调整为相同的长度train_seq_mat = sequence.pad_sequences(train_seq,maxlen=max_len)val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len)test_seq_mat = sequence.pad_sequences(test_seq,maxlen=max_len)
#-------------------------------第四步 建立LSTM模型并训练-------------------------------num_labels = 5model = Sequential()model.add(Embedding(max_words+1, 128, input_length=max_len))#model.add(Bidirectional(LSTM(128, dropout=0.3, recurrent_dropout=0.1)))model.add(Bidirectional(LSTM(128)))model.add(Dense(128, activation='relu'))model.add(Dropout(0.3))model.add(Dense(num_labels, activation='softmax'))model.summary()model.compile(loss="categorical_crossentropy", optimizer='adam', metrics=["accuracy"])
flag = "train"if flag == "train": print("模型训练") # 模型训练 model_fit = model.fit(train_seq_mat, train_y, batch_size=64, epochs=15, validation_data=(val_seq_mat,val_y), callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)] ) # 保存模型 model.save('bilstm_model.h5') del model # deletes the existing model # 计算时间 elapsed = (time.clock() - start) print("Time used:", elapsed) print(model_fit.history) else: print("模型预测") model = load_model('bilstm_model.h5') #--------------------------------------第五步 预测及评估-------------------------------- # 对测试集进行预测 test_pre = model.predict(test_seq_mat) confm = metrics.confusion_matrix(np.argmax(test_y,axis=1), np.argmax(test_pre,axis=1)) print(confm) print(metrics.classification_report(np.argmax(test_y,axis=1), np.argmax(test_pre,axis=1), digits=4)) print("accuracy", metrics.accuracy_score(np.argmax(test_y, axis=1), np.argmax(test_pre, axis=1))) # 结果存储 f1 = open("bilstm_test_pre.txt", "w") for n in np.argmax(test_pre, axis=1): f1.write(str(n) + "\n") f1.close()
f2 = open("bilstm_test_y.txt", "w") for n in np.argmax(test_y, axis=1): f2.write(str(n) + "\n") f2.close()
plt.figure(figsize=(8,8)) sns.heatmap(confm.T, square=True, annot=True, fmt='d', cbar=False, linewidths=.6, cmap="YlGnBu") plt.xlabel('True label',size = 14) plt.ylabel('Predicted label', size = 14) plt.xticks(np.arange(5)+0.5, Labname, size = 12) plt.yticks(np.arange(5)+0.5, Labname, size = 12) plt.savefig('bilstm_result.png') plt.show()
#--------------------------------------第六步 验证算法-------------------------------- # 使用tok对验证数据集重新预处理 val_seq = tok.texts_to_sequences(val_content) val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len) # 对验证集进行预测 val_pre = model.predict(val_seq_mat) print(metrics.classification_report(np.argmax(val_y,axis=1), np.argmax(val_pre,axis=1), digits=4)) print("accuracy", metrics.accuracy_score(np.argmax(val_y, axis=1), np.argmax(val_pre, axis=1))) # 计算时间 elapsed = (time.clock() - start) print("Time used:", elapsed)
2.实验结果
训练输出结果如下图所示：
模型训练Epoch 1/15 1/20 [>.............................] - ETA: 40s - loss: 1.6114 - accuracy: 0.2031 2/20 [==>...........................] - ETA: 10s - loss: 1.6055 - accuracy: 0.2969 3/20 [===>..........................] - ETA: 10s - loss: 1.6015 - accuracy: 0.3281 4/20 [=====>........................] - ETA: 10s - loss: 1.5931 - accuracy: 0.3477 5/20 [======>.......................] - ETA: 10s - loss: 1.5914 - accuracy: 0.3469 6/20 [========>.....................] - ETA: 10s - loss: 1.5827 - accuracy: 0.3698 7/20 [=========>....................] - ETA: 10s - loss: 1.5785 - accuracy: 0.3884 8/20 [===========>..................] - ETA: 10s - loss: 1.5673 - accuracy: 0.4121 9/20 [============>.................] - ETA: 9s - loss: 1.5610 - accuracy: 0.414910/20 [==============>...............] - ETA: 9s - loss: 1.5457 - accuracy: 0.418711/20 [===============>..............] - ETA: 8s - loss: 1.5297 - accuracy: 0.414812/20 [=================>............] - ETA: 8s - loss: 1.5338 - accuracy: 0.412813/20 [==================>...........] - ETA: 7s - loss: 1.5214 - accuracy: 0.427914/20 [====================>.........] - ETA: 6s - loss: 1.5176 - accuracy: 0.428615/20 [=====================>........] - ETA: 5s - loss: 1.5100 - accuracy: 0.427116/20 [=======================>......] - ETA: 4s - loss: 1.5065 - accuracy: 0.425817/20 [========================>.....] - ETA: 3s - loss: 1.5021 - accuracy: 0.423718/20 [==========================>...] - ETA: 2s - loss: 1.4921 - accuracy: 0.428819/20 [===========================>..] - ETA: 1s - loss: 1.4822 - accuracy: 0.433420/20 [==============================] - ETA: 0s - loss: 1.4825 - accuracy: 0.432720/20 [==============================] - 33s 2s/step - loss: 1.4825 - accuracy: 0.4327 - val_loss: 1.4187 - val_accuracy: 0.4074
Time used: 38.565846900000004{'loss': [1.4825222492218018], 'accuracy': [0.4327155649662018], 'val_loss': [1.4187402725219727], 'val_accuracy': [0.40740740299224854]}>>>
最终预测结果如下所示：
模型预测[[36 18 37 1 18] [14 46 34 0 6] [ 8 29 73 0 10] [16 29 14 45 26] [47 15 33 0 95]] precision recall f1-score support
0 0.2975 0.3273 0.3117 110 1 0.3358 0.4600 0.3882 100 2 0.3822 0.6083 0.4695 120 3 0.9783 0.3462 0.5114 130 4 0.6129 0.5000 0.5507 190
accuracy 0.4538 650 macro avg 0.5213 0.4484 0.4463 650weighted avg 0.5474 0.4538 0.4624 650
accuracy 0.45384615384615384
precision recall f1-score support
0 0.9189 0.3864 0.5440 352 1 0.4766 0.4766 0.4766 107 2 0.0000 0.0000 0.0000 0 3 0.0000 0.0000 0.0000 0 4 0.0000 0.0000 0.0000 0
accuracy 0.4074 459 macro avg 0.2791 0.1726 0.2041 459weighted avg 0.8158 0.4074 0.5283 459
accuracy 0.4074074074074074Time used: 32.2772881
四.基于BiGRU的恶意家族检测
1.模型构建
该模型的基本步骤如下：
第一步 数据读取第二步 OneHotEncoder()编码第三步 使用Tokenizer对词组进行编码第四步 建立BiGRU模型并训练第五步 预测及评估第六步 验证算法
构建模型如下图所示：
完整代码如下所示：
# -*- coding: utf-8 -*-# By:Eastmount CSDN 2023-06-27import pickleimport pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn import metricsimport tensorflow as tffrom sklearn.preprocessing import LabelEncoder,OneHotEncoderfrom keras.models import Modelfrom keras.layers import GRU, LSTM, Activation, Dense, Dropout, Input, Embeddingfrom keras.layers import Convolution1D, MaxPool1D, Flattenfrom keras.optimizers import RMSpropfrom keras.layers import Bidirectionalfrom keras.preprocessing.text import Tokenizerfrom keras.preprocessing import sequencefrom keras.callbacks import EarlyStoppingfrom keras.models import load_modelfrom keras.models import Sequentialfrom keras.layers.merge import concatenateimport time
start = time.clock()
#---------------------------------------第一步 数据读取------------------------------------# 读取测数据集train_df = pd.read_csv("..\\train_dataset.csv")val_df = pd.read_csv("..\\val_dataset.csv")test_df = pd.read_csv("..\\test_dataset.csv")print(train_df.head())
# 解决中文显示问题plt.rcParams['font.sans-serif'] = ['KaiTi']plt.rcParams['axes.unicode_minus'] = False
#---------------------------------第二步 OneHotEncoder()编码---------------------------------# 对数据集的标签数据进行编码 (no apt md5 api)train_y = train_df.aptval_y = val_df.apttest_y = test_df.aptle = LabelEncoder()train_y = le.fit_transform(train_y).reshape(-1,1)val_y = le.transform(val_y).reshape(-1,1)test_y = le.transform(test_y).reshape(-1,1)Labname = le.classes_
# 对数据集的标签数据进行one-hot编码ohe = OneHotEncoder()train_y = ohe.fit_transform(train_y).toarray()val_y = ohe.transform(val_y).toarray()test_y = ohe.transform(test_y).toarray()
#-------------------------------第三步 使用Tokenizer对词组进行编码-------------------------------# 使用Tokenizer对词组进行编码max_words = 2000max_len = 300tok = Tokenizer(num_words=max_words)
# 提取token：apitrain_value = train_df.apitrain_content = [str(a) for a in train_value.tolist()]val_value = val_df.apival_content = [str(a) for a in val_value.tolist()]test_value = test_df.apitest_content = [str(a) for a in test_value.tolist()]tok.fit_on_texts(train_content)print(tok)
# 保存训练好的Tokenizer和导入with open('tok.pickle', 'wb') as handle: pickle.dump(tok, handle, protocol=pickle.HIGHEST_PROTOCOL)with open('tok.pickle', 'rb') as handle: tok = pickle.load(handle)
# 使用tok.texts_to_sequences()将数据转化为序列train_seq = tok.texts_to_sequences(train_content)val_seq = tok.texts_to_sequences(val_content)test_seq = tok.texts_to_sequences(test_content)
# 将每个序列调整为相同的长度train_seq_mat = sequence.pad_sequences(train_seq,maxlen=max_len)val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len)test_seq_mat = sequence.pad_sequences(test_seq,maxlen=max_len)
#-------------------------------第四步 建立GRU模型并训练-------------------------------num_labels = 5model = Sequential()model.add(Embedding(max_words+1, 256, input_length=max_len))#model.add(Bidirectional(GRU(128, dropout=0.2, recurrent_dropout=0.1)))model.add(Bidirectional(GRU(256)))model.add(Dense(256, activation='relu'))model.add(Dropout(0.4))model.add(Dense(num_labels, activation='softmax'))model.summary()model.compile(loss="categorical_crossentropy", optimizer='adam', metrics=["accuracy"])
flag = "train"if flag == "train": print("模型训练") # 模型训练 model_fit = model.fit(train_seq_mat, train_y, batch_size=64, epochs=15, validation_data=(val_seq_mat,val_y), callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.005)] ) # 保存模型 model.save('gru_model.h5') del model # deletes the existing model # 计算时间 elapsed = (time.clock() - start) print("Time used:", elapsed) print(model_fit.history) else: print("模型预测") model = load_model('gru_model.h5') #--------------------------------------第五步 预测及评估-------------------------------- # 对测试集进行预测 test_pre = model.predict(test_seq_mat) confm = metrics.confusion_matrix(np.argmax(test_y,axis=1), np.argmax(test_pre,axis=1)) print(confm) print(metrics.classification_report(np.argmax(test_y,axis=1), np.argmax(test_pre,axis=1), digits=4)) print("accuracy", metrics.accuracy_score(np.argmax(test_y, axis=1), np.argmax(test_pre, axis=1))) # 结果存储 f1 = open("gru_test_pre.txt", "w") for n in np.argmax(test_pre, axis=1): f1.write(str(n) + "\n") f1.close()
f2 = open("gru_test_y.txt", "w") for n in np.argmax(test_y, axis=1): f2.write(str(n) + "\n") f2.close()
plt.figure(figsize=(8,8)) sns.heatmap(confm.T, square=True, annot=True, fmt='d', cbar=False, linewidths=.6, cmap="YlGnBu") plt.xlabel('True label',size = 14) plt.ylabel('Predicted label', size = 14) plt.xticks(np.arange(5)+0.5, Labname, size = 12) plt.yticks(np.arange(5)+0.5, Labname, size = 12) plt.savefig('gru_result.png') plt.show()
#--------------------------------------第六步 验证算法-------------------------------- # 使用tok对验证数据集重新预处理 val_seq = tok.texts_to_sequences(val_content) val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len) # 对验证集进行预测 val_pre = model.predict(val_seq_mat) print(metrics.classification_report(np.argmax(val_y,axis=1), np.argmax(val_pre,axis=1), digits=4)) print("accuracy", metrics.accuracy_score(np.argmax(val_y, axis=1), np.argmax(val_pre, axis=1))) # 计算时间 elapsed = (time.clock() - start) print("Time used:", elapsed)
2.实验结果
训练输出结果如下图所示：
模型训练Epoch 1/15
1/20 [>.............................] - ETA: 47s - loss: 1.6123 - accuracy: 0.1875 2/20 [==>...........................] - ETA: 18s - loss: 1.6025 - accuracy: 0.2656 3/20 [===>..........................] - ETA: 18s - loss: 1.5904 - accuracy: 0.3333 4/20 [=====>........................] - ETA: 18s - loss: 1.5728 - accuracy: 0.3867 5/20 [======>.......................] - ETA: 17s - loss: 1.5639 - accuracy: 0.4094 6/20 [========>.....................] - ETA: 17s - loss: 1.5488 - accuracy: 0.4375 7/20 [=========>....................] - ETA: 16s - loss: 1.5375 - accuracy: 0.4397 8/20 [===========>..................] - ETA: 16s - loss: 1.5232 - accuracy: 0.4434 9/20 [============>.................] - ETA: 15s - loss: 1.5102 - accuracy: 0.435810/20 [==============>...............] - ETA: 14s - loss: 1.5014 - accuracy: 0.425011/20 [===============>..............] - ETA: 13s - loss: 1.5053 - accuracy: 0.423312/20 [=================>............] - ETA: 12s - loss: 1.5022 - accuracy: 0.423213/20 [==================>...........] - ETA: 11s - loss: 1.4913 - accuracy: 0.427914/20 [====================>.........] - ETA: 9s - loss: 1.4912 - accuracy: 0.4286 15/20 [=====================>........] - ETA: 8s - loss: 1.4841 - accuracy: 0.436516/20 [=======================>......] - ETA: 7s - loss: 1.4720 - accuracy: 0.440417/20 [========================>.....] - ETA: 5s - loss: 1.4669 - accuracy: 0.437518/20 [==========================>...] - ETA: 3s - loss: 1.4636 - accuracy: 0.434919/20 [===========================>..] - ETA: 1s - loss: 1.4544 - accuracy: 0.438320/20 [==============================] - ETA: 0s - loss: 1.4509 - accuracy: 0.440020/20 [==============================] - 44s 2s/step - loss: 1.4509 - accuracy: 0.4400 - val_loss: 1.3812 - val_accuracy: 0.3660
Time used: 49.7057119{'loss': [1.4508591890335083], 'accuracy': [0.4399677813053131], 'val_loss': [1.381193995475769], 'val_accuracy': [0.3660130798816681]}
最终预测结果如下所示：
模型预测[[ 30 8 9 17 46] [ 13 50 9 13 15] [ 10 4 58 29 19] [ 11 8 8 73 30] [ 25 3 23 14 125]] precision recall f1-score support
0 0.3371 0.2727 0.3015 110 1 0.6849 0.5000 0.5780 100 2 0.5421 0.4833 0.5110 120 3 0.5000 0.5615 0.5290 130 4 0.5319 0.6579 0.5882 190
accuracy 0.5169 650 macro avg 0.5192 0.4951 0.5016 650weighted avg 0.5180 0.5169 0.5120 650
accuracy 0.5169230769230769
precision recall f1-score support
0 0.8960 0.3182 0.4696 352 1 0.7273 0.5234 0.6087 107 2 0.0000 0.0000 0.0000 0 3 0.0000 0.0000 0.0000 0 4 0.0000 0.0000 0.0000 0
accuracy 0.3660 459 macro avg 0.3247 0.1683 0.2157 459weighted avg 0.8567 0.3660 0.5020 459
accuracy 0.3660130718954248Time used: 60.106339399999996
五.基于CNN+BiLSTM和注意力的恶意家族检测
1.模型构建
该模型的基本步骤如下：
第一步 数据读取第二步 OneHotEncoder()编码第三步 使用Tokenizer对词组进行编码第四步 建立Attention机制第五步 建立Attention+CNN+BiLSTM模型并训练第六步 预测及评估第七步 验证算法
构建模型如下图所示：
Model: "model"__________________________________________________________________________________________________Layer (type) Output Shape Param # Connected to ==================================================================================================inputs (InputLayer) [(None, 100)] 0 __________________________________________________________________________________________________embedding (Embedding) (None, 100, 256) 256256 inputs[0][0] __________________________________________________________________________________________________conv1d (Conv1D) (None, 100, 256) 196864 embedding[0][0] __________________________________________________________________________________________________conv1d_1 (Conv1D) (None, 100, 256) 262400 embedding[0][0] __________________________________________________________________________________________________conv1d_2 (Conv1D) (None, 100, 256) 327936 embedding[0][0] __________________________________________________________________________________________________max_pooling1d (MaxPooling1D) (None, 25, 256) 0 conv1d[0][0] __________________________________________________________________________________________________max_pooling1d_1 (MaxPooling1D) (None, 25, 256) 0 conv1d_1[0][0] __________________________________________________________________________________________________max_pooling1d_2 (MaxPooling1D) (None, 25, 256) 0 conv1d_2[0][0] __________________________________________________________________________________________________concatenate (Concatenate) (None, 25, 768) 0 max_pooling1d[0][0] max_pooling1d_1[0][0] max_pooling1d_2[0][0] __________________________________________________________________________________________________bidirectional (Bidirectional) (None, 25, 256) 918528 concatenate[0][0] __________________________________________________________________________________________________dense (Dense) (None, 25, 128) 32896 bidirectional[0][0] __________________________________________________________________________________________________dropout (Dropout) (None, 25, 128) 0 dense[0][0] __________________________________________________________________________________________________attention_layer (AttentionLayer (None, 128) 6500 dropout[0][0] __________________________________________________________________________________________________dense_1 (Dense) (None, 5) 645 attention_layer[0][0] ==================================================================================================Total params: 2,002,025Trainable params: 1,745,769Non-trainable params: 256,256
完整代码如下所示：
# -*- coding: utf-8 -*-# By:Eastmount CSDN 2023-06-27import pickleimport pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsimport tensorflow as tffrom sklearn import metricsfrom sklearn.preprocessing import LabelEncoder,OneHotEncoderfrom keras.models import Modelfrom keras.layers import LSTM, GRU, Activation, Dense, Dropout, Input, Embeddingfrom keras.layers import Convolution1D, MaxPool1D, Flattenfrom keras.optimizers import RMSpropfrom keras.layers import Bidirectionalfrom keras.preprocessing.text import Tokenizerfrom keras.preprocessing import sequencefrom keras.callbacks import EarlyStoppingfrom keras.models import load_modelfrom keras.models import Sequentialfrom keras.layers.merge import concatenateimport time
start = time.clock()
#---------------------------------------第一步 数据读取------------------------------------# 读取测数据集train_df = pd.read_csv("..\\train_dataset.csv")val_df = pd.read_csv("..\\val_dataset.csv")test_df = pd.read_csv("..\\test_dataset.csv")print(train_df.head())
# 解决中文显示问题plt.rcParams['font.sans-serif'] = ['KaiTi']plt.rcParams['axes.unicode_minus'] = False
#---------------------------------第二步 OneHotEncoder()编码---------------------------------# 对数据集的标签数据进行编码 (no apt md5 api)train_y = train_df.aptval_y = val_df.apttest_y = test_df.aptle = LabelEncoder()train_y = le.fit_transform(train_y).reshape(-1,1)val_y = le.transform(val_y).reshape(-1,1)test_y = le.transform(test_y).reshape(-1,1)Labname = le.classes_
# 对数据集的标签数据进行one-hot编码ohe = OneHotEncoder()train_y = ohe.fit_transform(train_y).toarray()val_y = ohe.transform(val_y).toarray()test_y = ohe.transform(test_y).toarray()
#-------------------------------第三步 使用Tokenizer对词组进行编码-------------------------------# 使用Tokenizer对词组进行编码max_words = 1000max_len = 100tok = Tokenizer(num_words=max_words)
# 提取token：apitrain_value = train_df.apitrain_content = [str(a) for a in train_value.tolist()]val_value = val_df.apival_content = [str(a) for a in val_value.tolist()]test_value = test_df.apitest_content = [str(a) for a in test_value.tolist()]tok.fit_on_texts(train_content)print(tok)
# 保存训练好的Tokenizer和导入with open('tok.pickle', 'wb') as handle: pickle.dump(tok, handle, protocol=pickle.HIGHEST_PROTOCOL)with open('tok.pickle', 'rb') as handle: tok = pickle.load(handle)
# 使用tok.texts_to_sequences()将数据转化为序列train_seq = tok.texts_to_sequences(train_content)val_seq = tok.texts_to_sequences(val_content)test_seq = tok.texts_to_sequences(test_content)
# 将每个序列调整为相同的长度train_seq_mat = sequence.pad_sequences(train_seq,maxlen=max_len)val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len)test_seq_mat = sequence.pad_sequences(test_seq,maxlen=max_len)
#-------------------------------第四步 建立Attention机制-------------------------------"""由于Keras目前还没有现成的Attention层可以直接使用，我们需要自己来构建一个新的层函数。 Keras自定义的函数主要分为四个部分，分别是： init：初始化一些需要的参数 bulid：具体来定义权重是怎么样的 call：核心部分，定义向量是如何进行运算的 compute_output_shape：定义该层输出的大小
推荐文章 https://blog.csdn.net/huanghaocs/article/details/95752379推荐文章 https://zhuanlan.zhihu.com/p/29201491"""# Hierarchical Model with Attentionfrom keras import initializersfrom keras import constraintsfrom keras import activationsfrom keras import regularizersfrom keras import backend as Kfrom keras.engine.topology import Layer
K.clear_session()
class AttentionLayer(Layer): def __init__(self, attention_size=None, **kwargs): self.attention_size = attention_size super(AttentionLayer, self).__init__(**kwargs) def get_config(self): config = super().get_config() config['attention_size'] = self.attention_size return config def build(self, input_shape): assert len(input_shape) == 3 self.time_steps = input_shape[1] hidden_size = input_shape[2] if self.attention_size is None: self.attention_size = hidden_size self.W = self.add_weight(name='att_weight', shape=(hidden_size, self.attention_size), initializer='uniform', trainable=True) self.b = self.add_weight(name='att_bias', shape=(self.attention_size,), initializer='uniform', trainable=True) self.V = self.add_weight(name='att_var', shape=(self.attention_size,), initializer='uniform', trainable=True) super(AttentionLayer, self).build(input_shape)
#解决方法: Attention The graph tensor has name: model/attention_layer/Reshape:0 #https://blog.csdn.net/weixin_54227557/article/details/129898614 def call(self, inputs): #self.V = K.reshape(self.V, (-1, 1)) V = K.reshape(self.V, (-1, 1)) H = K.tanh(K.dot(inputs, self.W) + self.b) #score = K.softmax(K.dot(H, self.V), axis=1) score = K.softmax(K.dot(H, V), axis=1) outputs = K.sum(score * inputs, axis=1) return outputs def compute_output_shape(self, input_shape): return input_shape[0], input_shape[2]
#-------------------------------第五步 建立Attention+CNN模型并训练-------------------------------# 构建TextCNN模型num_labels = 5inputs = Input(name='inputs',shape=[max_len], dtype='float64')layer = Embedding(max_words+1, 256, input_length=max_len, trainable=False)(inputs)cnn1 = Convolution1D(256, 3, padding='same', strides = 1, activation='relu')(layer)cnn1 = MaxPool1D(pool_size=4)(cnn1)cnn2 = Convolution1D(256, 4, padding='same', strides = 1, activation='relu')(layer)cnn2 = MaxPool1D(pool_size=4)(cnn2)cnn3 = Convolution1D(256, 5, padding='same', strides = 1, activation='relu')(layer)cnn3 = MaxPool1D(pool_size=4)(cnn3)
# 合并三个模型的输出向量cnn = concatenate([cnn1,cnn2,cnn3], axis=-1)
# BiLSTM+Attention#bilstm = Bidirectional(LSTM(100, dropout=0.2, recurrent_dropout=0.1, return_sequences=True))(cnn)bilstm = Bidirectional(LSTM(128, return_sequences=True))(cnn) #参数保持维度3layer = Dense(128, activation='relu')(bilstm)layer = Dropout(0.3)(layer)attention = AttentionLayer(attention_size=50)(layer)
output = Dense(num_labels, activation='softmax')(attention)model = Model(inputs=inputs, outputs=output)model.summary()model.compile(loss="categorical_crossentropy", optimizer='adam', metrics=["accuracy"])
flag = "test"if flag == "train": print("模型训练") # 模型训练 model_fit = model.fit(train_seq_mat, train_y, batch_size=128, epochs=15, validation_data=(val_seq_mat,val_y), callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0005)] )
# 保存模型 model.save('cnn_bilstm_model.h5') del model # deletes the existing model #计算时间 elapsed = (time.clock() - start) print("Time used:", elapsed) print(model_fit.history) else: print("模型预测") model = load_model('cnn_bilstm_model.h5', custom_objects={'AttentionLayer': AttentionLayer(50)}, compile=False)
#--------------------------------------第六步 预测及评估-------------------------------- # 对测试集进行预测 test_pre = model.predict(test_seq_mat) confm = metrics.confusion_matrix(np.argmax(test_y,axis=1),np.argmax(test_pre,axis=1)) print(confm) print(metrics.classification_report(np.argmax(test_y,axis=1), np.argmax(test_pre,axis=1), digits=4)) print("accuracy",metrics.accuracy_score(np.argmax(test_y,axis=1), np.argmax(test_pre,axis=1))) # 结果存储 f1 = open("cnn_bilstm_test_pre.txt", "w") for n in np.argmax(test_pre, axis=1): f1.write(str(n) + "\n") f1.close()
f2 = open("cnn_bilstm_test_y.txt", "w") for n in np.argmax(test_y, axis=1): f2.write(str(n) + "\n") f2.close()
plt.figure(figsize=(8,8)) sns.heatmap(confm.T, square=True, annot=True, fmt='d', cbar=False, linewidths=.6, cmap="YlGnBu") plt.xlabel('True label',size = 14) plt.ylabel('Predicted label', size = 14) plt.xticks(np.arange(5)+0.5, Labname, size = 12) plt.yticks(np.arange(5)+0.5, Labname, size = 12) plt.savefig('cnn_bilstm_result.png') plt.show()
#--------------------------------------第七步 验证算法-------------------------------- # 使用tok对验证数据集重新预处理，并使用训练好的模型进行预测 val_seq = tok.texts_to_sequences(val_content) val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len) # 对验证集进行预测 val_pre = model.predict(val_seq_mat) print(metrics.classification_report(np.argmax(val_y, axis=1), np.argmax(val_pre, axis=1), digits=4)) print("accuracy", metrics.accuracy_score(np.argmax(val_y, axis=1), np.argmax(val_pre, axis=1))) # 计算时间 elapsed = (time.clock() - start) print("Time used:", elapsed)
2.实验结果
训练输出结果如下图所示：
模型训练Epoch 1/15
1/10 [==>...........................] - ETA: 18s - loss: 1.6074 - accuracy: 0.2188 2/10 [=====>........................] - ETA: 2s - loss: 1.5996 - accuracy: 0.2383 3/10 [========>.....................] - ETA: 2s - loss: 1.5903 - accuracy: 0.2500 4/10 [===========>..................] - ETA: 2s - loss: 1.5665 - accuracy: 0.2793 5/10 [==============>...............] - ETA: 2s - loss: 1.5552 - accuracy: 0.2750 6/10 [=================>............] - ETA: 1s - loss: 1.5346 - accuracy: 0.2930 7/10 [====================>.........] - ETA: 1s - loss: 1.5229 - accuracy: 0.3103 8/10 [=======================>......] - ETA: 1s - loss: 1.5208 - accuracy: 0.3135 9/10 [==========================>...] - ETA: 0s - loss: 1.5132 - accuracy: 0.328110/10 [==============================] - ETA: 0s - loss: 1.5046 - accuracy: 0.340010/10 [==============================] - 9s 728ms/step - loss: 1.5046 - accuracy: 0.3400 - val_loss: 1.4659 - val_accuracy: 0.5599
Time used: 13.8141568{'loss': [1.5045626163482666], 'accuracy': [0.34004834294319153], 'val_loss': [1.4658586978912354], 'val_accuracy': [0.5599128603935242]}
最终预测结果如下所示：
模型预测[[ 56 13 1 0 40] [ 31 53 0 0 16] [ 54 47 3 1 15] [ 27 14 1 51 37] [ 39 16 8 2 125]] precision recall f1-score support
0 0.2705 0.5091 0.3533 110 1 0.3706 0.5300 0.4362 100 2 0.2308 0.0250 0.0451 120 3 0.9444 0.3923 0.5543 130 4 0.5365 0.6579 0.5910 190
accuracy 0.4431 650 macro avg 0.4706 0.4229 0.3960 650weighted avg 0.4911 0.4431 0.4189 650
accuracy 0.4430769230769231
havior. precision recall f1-score support
0 0.8571 0.5625 0.6792 352 1 0.6344 0.5514 0.5900 107 2 0.0000 0.0000 0.0000 0 4 0.0000 0.0000 0.0000 0
accuracy 0.5599 459 macro avg 0.3729 0.2785 0.3173 459weighted avg 0.8052 0.5599 0.6584 459
accuracy 0.5599128540305011Time used: 23.0178675
六.总结
写到这里这篇文章就结束，希望对您有所帮助。忙碌的五月、六月，真的很忙，项目本子论文毕业，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.恶意软件分析
- 1.静态特征
- 2.动态特征
- 二.基于CNN的恶意家族检测
- 1.数据集
- 2.模型构建
- 3.实验结果
- 三.基于BiLSTM的恶意家族检测
- 1.模型构建
- 2.实验结果
- 四.基于BiGRU的恶意家族检测
- 1.模型构建
- 2.实验结果
- 五.基于CNN+BiLSTM和注意力的恶意家族检测
- 1.模型构建
- 2.实验结果
VSole
网络安全专家