STATISTICS

Viewed2333

Downloads1779

Intrusion Detection Model Based on CNN and BiGRU Fused Neural Network
[1]ZHANG Anlin,ZHANG Qikun,HUANG Daoying,et al.Intrusion Detection Model Based on CNN and BiGRU Fused Neural Network[J].Journal of Zhengzhou University (Engineering Science),2022,43(03):37-43.[doi:10.13705/j.issn.1671-6833.2022.03.003]
Copy
References:
[1] FERNÁNDEZ G C, XU S H. A case study on using deep learning for network intrusion detection [ C ] / / MILCOM 2019 IEEE Military Communications Conference (MILCOM) . Piscataway:IEEE,2019:1-6.
 [2] 张蕾,崔勇,刘静,等. 机器学习在网络空间安全研究 中的应用[J]. 计算机学报,2018,41(9):1943-1975.
 [3] 张玉清,董颖,柳彩云,等. 深度学习应用于网络空 间安全的现状、趋势与展望 [ J] . 计 算 机 研 究 与 发 展,2018,55(6) :1117-1142. 
[4] LECUN Y,BOSER B,DENKER J S,et al. Backpropagation applied to handwritten zip code recognition[ J] . Neural computation,1989,1(4) :541-551.
 [5] ROY S S,MALLIK A,GULATI R,et al. A deep learning based artificial neural network approach for intrusion detection[ J] . Mathematics and computing,2017, 655:44-53. 
[6] WANG W,ZHU M,ZENG X W,et al. Malware traffic classification using convolutional neural network for representation learning [ C] / / 2017 International Conference on Information Networking ( ICOIN) . Piscataway:IEEE,2017:712-717.
 [7] NASEER S, SALEEM Y,KHALID S, et al. Enhanced network anomaly detection based on deep neural networks[ J] . IEEE access,2018,6:48231-48246. 
[8] KIM J,KIM J,LE T T H,et al. Long short term memory recurrent neural network classifier for intrusion detection[ C ] / / 2016 International Conference on Platform Technology and Service ( PlatCon) . Piscataway: IEEE,2016:1-5. 
[9] PUTCHALA M K. Deep learning approach for intrusion detection system (IDS) in the internet of things ( IoT) network using gated recurrent neural networks (GRU) [D]. Dayton:Wright State University, 2017. 
[10] 王伟. 基于深度学习的网络流量分类及异常检测方 法研究[D] . 合肥:中国科学技术大学,2018.
 [11] YIN C L,ZHU Y F,FEI J L,et al. A deep learning approach for intrusion detection using recurrent neural networks[ J] . IEEE access,2017,5:21954-21961.
 [12] 张勇东,陈思洋,彭雨荷,等. 基于深度学习的网络 入侵检测研究综述 [ J] . 广州大学学报 ( 自然科学 版) ,2019,18(3) :17-26. 
[13] 陈洁,邵志清,张欢欢,等. 基于并行混合神经网络 模型的短文本情感分析[ J] . 计算机应用,2019,39 (8) :2192-2197. 
[14] SCHUSTER M,PALIWAL K K. Bidirectional recurrent neural networks [ J] . IEEE transactions on signal processing,1997,45(11) :2673-2681. 
[15] SHARAFALDIN I, LASHKARI A H, GHORBANI A A. Toward generating a new intrusion detection dataset and intrusion traffic characterization[ C] / / Proceedings of the 4th International Conference on Information Systems Security and Privacy. Funchal: ICISSP, 2018: 108-116. 
[16] PANIGRAHI R, BORAH S. A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems [ J ] . International journal of engineering & technology, 2018, 7: 479-482.
 [17] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique [ J] . Journal of artificial intelligence research, 2002, 16:321-357.
 [18] BATISTA G E A P A,PRATI R C,MONARD M C. A study of the behavior of several methods for balancing machine learning training data[ J] . ACM SIGKDD explorations newsletter,2004,6(1) :20-29. 
[19] 李勇,金庆雨,张青川. 融合位置注意力机制和改进 BLSTM 的食 品 评 论 情 感 分 析 [ J] . 郑 州 大 学 学 报 (工学版) ,2020,41(1) :58-62.
 [20] 朱张莉,饶元,吴渊,等. 注意力机制在深度学习中 的研究进展[ J] . 中文信息学报,2019,33(6) :1-11. 
[21] DOGO E M,AFOLABI O J,NWULU N I,et al. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks [ C ] / / 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) . Piscataway:IEEE,2018:92-99
Similar References:
Memo

-

Last Update: 2022-05-02
Copyright © 2023 Editorial Board of Journal of Zhengzhou University (Engineering Science)