2023年11月10日21:16:41评论74 views字数 40310阅读134分22秒阅读模式

《当人工智能遇上安全》系列博客将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~

前文详细介绍如何学习提取的API序列特征，并构建机器学习算法实现恶意家族分类，这也是安全领域典型的任务或工作。这篇文章将讲解如何构建深度学习模型实现恶意软件家族分类，常见模型包括CNN、BiLSTM、BiGRU，结合注意力机制的CNN+BiLSTM。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！

文章目录：

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

六.总结

前文推荐：

作者的github资源：

https://github.com/eastmountyxz/AI-Security-Paper

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，一起加油喔！

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：

[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

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
AAAA	class1	352	242	110
BBBB	class2	335	235	100
CCCC	class3	363	243	120
DDDD	class4	293	163	130
EEEE	class5	548	358	190

数据集分为训练集和测试集，如下图所示：

[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

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。

[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

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：

[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

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：

[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

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：

模型预测[[ 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

[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

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：

[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

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

[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

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：

[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

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

[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

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

[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

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

作者提问如下，欢迎大家补充：

恶意软件或二进制常见的特征包括哪些？各自有哪些优缺点。
恶意软件转灰度图是常见的家族分类方法，它与本文提出的方法的优缺点是什么？
如何提取恶意软件CFG和ICFG呢？提取后又如何被机器学习模型学习？
常见的向量表征方法有哪些，各自有哪些特点？您能否实现Word2Vec的代码呢？
机器学习和深度学习的联系及区别是什么？如果构建深度学习模型学习API序列，其恶意家族检测效果如何？
恶意软件家族分类或恶意代码检测发展到如今现状如何？工业界和学术界各种有哪些特点及局限，如何更好地关联来促进领域发展？
二进制方向是否还有更好的创新或突破性方法？其鲁棒性、语义增强、可解释性如何提升。
如何实现未知家族的恶意软件检测，又如何实现高威胁恶意软件的溯源呢？
恶意软件检测如何更好地和底层硬件及编译器融合？以及如何对抗变种、混淆及对抗。
恶意软件检测能通过chatGPT技术快速生成变种吗？又如何对抗该技术的发展。

三月应该是今年最忙碌的一个月了，好多事情。希望一切顺利，更希望四月后能沉下心来读论文和写论文。然而四月、五月同样忙碌，继续加油，只争朝夕，娜璋白首。

感谢大家的支持和关注。继续加油！感恩，娜璋白首。

(By:Eastmount 2023-11-10 夜于火星)

参考文章如下，感谢这些大佬。

[1] https://github.com/cuckoosandbox/cuckoo
[2] https://github.com/kevoreilly/CAPEv2
[3] https://capesandbox.com
[4] https://capev2.readthedocs.io/en/latest/
[5] https://capev2.readthedocs.io/en/latest/usage/submit.html
[6] Cuckoo变种-CAPE简介 - Threathunter

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[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

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

六.总结

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