Malicious Domain Name Training Data Generation Technology Based on Improved CNN Model

doi:10.3969/j.issn.1671-1122.2021.10.010

Abstract

Abstract:

In recent years, new botnets have begun to use command and control (C&C) server communication to attack and use domain name generation algorithms (DGA) to avoid detection. The traditional algorithm of domain name generation has some disadvantages,such as low addressing efficiency and easy detection due to the corresponding code traffic of a large number of domains. In this paper, we use the self-attention mechanism of BI-LSTM to generate malicious domain name by improving the traditional CNN model and combining with the related ideas of text generation. The final results show that the domain name data generated by this method can be used as real domain name data in the comparative experiment, which improves the efficiency of detecting malicious domain name.

Key words: malicious domain name, convolutional neural network, botnet, machine learning

CLC Number:

TP309

MA Xiao, CAI Manchun, LU Tianliang. Malicious Domain Name Training Data Generation Technology Based on Improved CNN Model[J]. Netinfo Security, 2021, 21(10): 69-75.

Figures/Tables 7

References 23

[1]	YUAN Chen, QIAN Liping, ZHANG Hui, et al. Generation of Malicious Domain Training Data Based on Generative Adversarial Network[J]. Application Research of Computers, 2019, 36(5):1540-1543.
	袁辰, 钱丽萍, 张慧, 等. 基于生成对抗网络的恶意域名训练数据生成[J]. 计算机应用研究, 2019, 36(5):1540-1543.
[2]	YERIMA S, ALZAYLAEE M, SHAJAN A. Deep Learning Techniques for Android Botnet Detection[J]. Electronics (Basel), 2021, 10(4):519-535.
[3]	LIN Shaoqing, ZHONG Shangping, CHENG Kaizhi, et al. Method with Pre-trained Word Vectors for Detecting Wordlist-based Malicious Domain Names[J]. Journal of Physics Conference Series, 2021, 1757(1):12171-12177.
[4]	TRAN D, MAC H, TONG V, et al. A LSTM Based Framework for Handling Multiclass Imbalance in DGA Botnet Detection[J]. Neurocomputing, 2018, 275(1):2401-2413. doi: 10.1016/j.neucom.2017.11.018 URL
[5]	WOODBRIDGE J, ANDERSON H S, AHUJA A, et al. Predicting Domain Generation Algorithms with Long Short-term Memory Networks[EB/OL]. https://arxiv.org/abs/1611. 00791, 2016-11-02.
[6]	XU Guotian. Generation Algorithm Crack Based on DGA Domain Name of Malicious Program[J]. Netinfo Security, 2017, 17(9):26-29.
	徐国天. 基于DGA的恶意程序域名生成算法破解[J]. 信息网络安全, 2017, 17(9):26-29.
[7]	YANG Luhui, LIU Guangjie, ZHAI Jiangtao, et al. Improved Algorithm for Detection of the Malicious Domain Name Based on the Convolutional Neural Network[J]. Journal of Xidian University, 2020, 47(1):37-43.
	杨路辉, 刘光杰, 翟江涛, 等. 一种改进的卷积神经网络恶意域名检测算法[J]. 西安电子科技大学学报, 2020, 47(1):37-43.
[8]	CUCCHIARELLI A, MORBIDONI C, SPALAZZI L, et al. Algorithmically Generated Malicious Domain Names Detection Based on N-grams Features[J]. Expert Systems with Applications, 2021, 170(5):114551-114566. doi: 10.1016/j.eswa.2020.114551 URL
[9]	WANG Yuanyuan, WU Chunjiang, LIU Qihe, et al. Overview of Malicious Domain Name Detection and Appliciation[J]. Computer Applications and Software, 2019, 36(9):310-316.
	王媛媛, 吴春江, 刘启和, 等. 恶意域名检测研究与应用综述[J]. 计算机应用与软件, 2019, 36(9):310-316.
[10]	CHEN Zhao, XU Ruifeng, GUI Lin, et al. Combining Convolutional Neural Networks and Word Sentiment Sequences Features for Chinese Text Sentiment Analysis[J]. Journal of Chinese Information Processing, 2015, 29(6):172-178.
	陈钊, 徐睿峰, 桂林, 等. 结合卷积神经网络和词语情感序列特征的中文情感分析[J]. 中文信息学报, 2015, 29(6):172-178.
[11]	WANG Jingbo, MENG Lingjun. Convolutional Neural Network Face Detection Algorithm[J]. Application of Electronic Technique, 2020, 46(1):34-38.
	王静波, 孟令军. 卷积神经网络人脸检测算法[J]. 电子技术应用, 2020, 46(1):34-38.
[12]	LU Yubo, LIU Derun, CAI Yichao, et al. Chinese Weibo Sentiment Classification Based on cw2vec and CNN-BiLSTM Attention Model[J]. Software Guide, 2021, 20(3):51-56.
	卢昱波, 刘德润, 蔡奕超, 等. 基于cw2vec与CNN-BiLSTM注意力模型的中文微博情感分类[J]. 软件导刊, 2021, 20(3):51-56.
[13]	CAO Zhengzhi, YE Chunming. Prediction of Bearing Remaining useful Life Based on Parallel CNN-SE-Bi-LSTM[EB/OL]. https://doi.org/10.19734/j.issn.1001-3695.2020.08.0224, 2021-03-05.
	曹正志, 叶春明. 基于并联CNN-SE-Bi-LSTM的轴承剩余使用寿命预测[EB/OL]. https://doi.org/10.19734/j.issn.1001-3695.2020.08.0224, 2021-03-05.
[14]	ZHONG Rui, WANG Rui, ZOU Yang, et al. Graph Attention Networks Adjusted Bi-LSTM for Video Summarization[J]. IEEE Signal Processing Letters, 2021, 28(1):663-667. doi: 10.1109/LSP.2021.3066349 URL
[15]	YIN Liangliang, SUN Hongguang, JIA Huiting, et al. Text Sentiment Analysis Method Based on a Two-way Multi-dimensional Selfattention Mechanism[J]. China Computer and Communication, 2019, 7(9):45-48.
	尹良亮, 孙红光, 贾慧婷, 等. 基于双向多维度自注意力机制的文本情感分析方法[J]. 信息与电脑(理论版), 2019, 7(9):45-48.
[16]	DU Lin, CAO Dong, LIN Shuyuan, et al. Extraction and Automatic Classification of TCM Medical Records Based on Attention Mechanism of BERT and Bi-LSTM[J]. Computer Science, 2020, 47(S2):416-420.
	杜琳, 曹东, 林树元, 等. 基于BERT与Bi-LSTM融合注意力机制的中医病历文本的提取与自动分类[J]. 计算机科学, 2020, 47(S2):416-420.
[17]	CHENG Hua, CAI Jing, FANG Yiquan. RL-Gen: A Character Level Text Generation Framework with Reinforcement Learning in Domain Generation Algorithm Case [C]//Springer. International Conference on Neural Information Processing, December 12-15, 2019, Sydney, Australia. Berlin: Springer, 2019: 690-697.
[18]	LUO Quan, LIU Zhi. A Word Operation Exercises Auto-scoring Algorithm Based on Levenshtein Distance[J]. Modern Computer, 2020, 32(1):90-93.
	罗泉, 刘芝. 基于Levenshtein距离的Word操作题自动评分算法[J]. 现代计算机, 2020, 32(1):90-93.
[19]	CAMARA A, DAN G. Research on Information Processing in Management Information System of Soguifret S.a.r.l Company in Guinea Based Levenshtein Algorithm[J]. Journal of Physics Conference Series, 2021, 1820(1):12155-12161.
[20]	WANG Jing, BAO Gui. Comparison of Bayesian Statistics with Traditional Statistical Methods[J]. Statistics and Decision, 2021, 37(1):24-29.
	王婧, 鲍贵. 贝叶斯统计与传统统计方法的比较[J]. 统计与决策, 2021, 37(1):24-29.
[21]	LI Jing. Land Complex was Classified Based on an Object-oriented Approach Based on Random Forest Versus J48 Decision Tree[J]. Technology Innovation and Application, 2021, 1(10):136-139.
	李靖. 基于随机森林与J48决策树的面向对象方法的土地覆被分类[J]. 科技创新与应用, 2021, 1(10):136-139.
[22]	ZHENG Xiandong, FENG Wenlong, HUANG Mengxing, et al. Optimization of PBFT Algorithm Based on Improved C4.5[J]. Mathematical Problems in Engineering, 2021, 2021(2):1-7.
[23]	WANG Yanguang, ZHU Hongbin, XU Weichao. A Review on ROC Curve and Analysis[J]. Journal of Guangdong University of Technology, 2021, 38(1):46-53.
	王彦光, 朱鸿斌, 徐维超. ROC曲线及其分析方法综述[J]. 广东工业大学学报, 2021, 38(1):46-53.

实验平台	环境配置
操作系统	Windows10
内存	8 GB
处理器	Intel Corei5-6300HQ CPU@2.30 GHz
深度学习框架	Tensorflow1.14.0
机器学习平台	Anaconda4.1.12

分类器	贝叶斯	J48	随机森林
准确率	0.975	0.967	0.981
召回率	0.974	0.965	0.977
F-measure	0.974	0.966	0.978
AUC	0.978	0.966	0.988
正确率	0.998	0.999	0.998
错误率	0.002	0.001	0.002

分类器	贝叶斯	J48	随机森林
准确率	0.976	0.965	0.982
召回率	0.977	0.963	0.978
F-measure	0.976	0.964	0.980
AUC	0.978	0.966	0.987
正确率	0.996	0.998	0.995
错误率	0.004	0.002	0.005

分类器	贝叶斯	J48	随机森林
准确率	0.976	0.965	0.982
召回率	0.975	0.966	0.978
F-measure	0.975	0.965	0.979
AUC	0.977	0.966	0.989
正确率	0.998	0.998	0.997
错误率	0.002	0.002	0.003