Journal of Information Security Reserach ›› 2022, Vol. 8 ›› Issue (12): 1163-.
Previous Articles Next Articles
Online:
2022-12-03
Published:
2022-12-01
黄屿璁1张潮2吕鑫1,3曾涛1王鑫元1丁辰龙1
通讯作者:
黄屿璁
博士研究生.主要研究方向为入侵检测、网络安全.
huangyc89757@163.com
作者简介:
黄屿璁
博士研究生.主要研究方向为入侵检测、网络安全.
huangyc89757@163.com
张潮
博士,高级工程师.主要研究方向为水利信息化、网络安全.
zhangchao@mwr.gov.cn
吕鑫
博士,副教授.主要研究方向为网络与信息安全、大数据分析与隐私保护.
lvxin@hhu.edu.cn
曾涛
博士研究生.主要研究方向为深度学习、入侵检测.
tzeng.nj@hhu.edu.cn
王鑫元
博士研究生.主要研究方向为入侵检测、隐私保护.
wxyhhu@hhu.edu.cn
丁辰龙
博士研究生.主要研究方向为网络安全.
policeasy@hhu.edu.cn
[1]张赛男, 孙彪. 基于机器学习的网络异常检测方法综述[J]. 吉林大学学报: 信息科学版, 2021, 39(6): 732742[2]Liu H, Lang B. Machine learning and deep learning methods for intrusion detection systems: A survey[J]. Applied Sciences, 2019, 9(20): 43964396[3]Chamou D, Toupas P, Ketzaki E, et al. Intrusion detection system based on network traffic using deep neural networks[C] Proc of the 24th Int Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). Piscataway, NJ: IEEE, 2019: 16[4]Otter D W, Medina J R, Kalita J K. A survey of the usages of deep learning for natural language processing[J]. IEEE Trans on Neural Networks and Learning Systems, 2019, 32(2): 604624[5]Almiani M, AbuGhazleh A, AlRahayfeh A, et al. Deep recurrent neural network for IoT intrusion detection system[EBOL]. [20220402]. https:www.researchgate.netpublication337492444_Deep_Recurrent_Neural_Network_For_IoT_Intrusion_Detection_System[6]Kingma D P, Welling M. Stochastic gradient VB and the variational autoencoder[EBOL]. (20140501) [20220402]. https:arxiv.orgabs1312.6114v7[7]Duan T, Tian Y, Zhang H, et al. Intelligent processing of intrusion detection data[J]. IEEE Access, 2020, 8(4): 7833078342[8]Yang Y, Zheng K, Wu C, et al. Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks[J]. Applied Sciences, 2019, 9(2): 125[9]唐贤伦, 杜一铭, 刘雨微, 等. 基于条件深度卷积生成对抗网络的图像识别方法[J]. 自动化学报, 2018, 44(5): 855864[10]Gumusbas D, Yldrm T, Genovese A, et al. A comprehensive survey of databases and deep learning methods for cybersecurity and intrusion detection systems[J]. IEEE Systems Journal, 2021, 15(2): 17171731[11]Hu Z, Wang L, Qi L, et al. A novel wireless network intrusion detection method based on adaptive synthetic sampling and an improved convolutional neural network[J]. IEEE Access, 2020, 8(10): 195741195751[12]产院东, 郭乔进, 梁中岩, 等. 基于深度学习的入侵检测综述[J]. 信息化研究, 2021, 47(4): 17[13]蹇诗婕, 卢志刚, 杜丹, 等. 网络入侵检测技术综述[J]. 信息安全学报, 2020, 5(4): 96122[14]Genovese A, Piuri V, Plataniotis K N, et al. PalmNet: GaborPCA convolutional networks for touchless palmprint recognition[J]. IEEE Trans on Information Forensics and Security, 2019, 14(12): 31603174[15]Shibahara T, Yagi T, Akiyama M, et al. Efficient dynamic malware analysis based on network behavior using deep learning[C] Proc of the 2016 IEEE Global Communications Conf(GLOBECOM). Piscataway, NJ: IEEE, 2016: 17[16]David O E, Netanyahu N S. Deepsign: Deep learning for automatic malware signature generation and classification[C] Proc of the 2015 Int Joint Conf Neural Networks (IJCNN). Piscataway, NJ: IEEE, 2015: 18[17]Wang X, Yiu S M. A multitask learning model for malware classification with useful file access pattern from API call sequence[EBOL]. [20220402]. https:www.researchgate.netpublication309288495_A_multitask_learning_model_for_malware_classification_with_useful_file_access_pattern_from_API_call_sequence[18]YousefiAzar M, Varadharajan V, Hamey L, et al. Autoencoderbased feature learning for cyber security applications[C] Proc of the 2017 Int Joint Conf Neural Networks (IJCNN). Piscataway, NJ: IEEE, 2017: 38543861[19]Anderson H S, Woodbridge J, Filar B. DeepDGA: Adversariallytuned domain generation and detection[C] Proc of the 2016 ACM Workshop on Artificial Intelligence and Security. New York: ACM, 2016: 1321[20]Woodbridge J, Anderson H S, Ahuja A, et al. Predicting domain generation algorithms with long shortterm memory networks[EBOL]. [20220402]. http:www.covert.ioresearchpapersdeeplearningsecurityPredicting%20Domain%20Generation%20Algorithms%20with%20Long%20ShortTerm%20Memory%20Networks.pdf[21]Zeng F, Chang S, Wan X. Classification for DGAbased malicious domain names with deep learning architectures[J]. International Journal of Intelligent Information Systems, 2017, 6(6): 6771[22]Mac H, Tran D, Tong V, et al. DGA botnet detection using supervised learning methods[C] Proc of the 8th Int Symp on Information and Communication Technology. New York: ACM, 2017: 211218[23]Nasr M, Bahramali A, Houmansadr A. Deepcorr: Strong flow correlation attacks on TOR using deep learning[C] Proc of the 2018 ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2018: 19621976[24]Jiang J, Chen J, Choo K R, et al. A deep learning based online malicious URL and DNS detection scheme[G] SecureComm 2017: Security and Privacy in Communication Networks. Berlin: Springer, 2017: 438448[25]Khan R U, Zhang X, Alazab M, et al. An improved convolutional neural network model for intrusion detection in networks[C] Proc of 2019 Cybersecurity and Cyberforensics Conf(CCC). Piscataway, NJ: IEEE, 2019: 7477[26]Andresini G, Appice A, Malerba D. Nearest clusterbased intrusion detection through convolutional neural networks[EBOL]. (20210315) [20220402]. https:www.sciencedirect.comsciencearticleabspiiS0950705121000617[27]Shibahara T, Yamanishi K, Takata Y, et al. Malicious URL sequence detection using event denoising convolutional neural network[C] Proc of the 2017 IEEE Int Conf Communications (ICC). Piscataway, NJ: IEEE, 2017: 17[28]Suda H, Natsui M, Hanyu T. Systematic intrusion detection technique for an invehicle network based on timeseries feature extraction[C] Proc of the 48th IEEE Int Symp on MultipleValued Logic (ISMVL). Piscataway, NJ: IEEE, 2018: 5661[29]燕昺昊, 韩国栋. 基于深度循环神经网络和改进SMOTE算法的组合式入侵检测模型[J]. 网络与信息安全学报, 2018, 4(7): 4859[30]Hou H, Xu Y, Chen M, et al. Hierarchical long shortterm memory network for cyberattack detection[J]. IEEE Access, 2020, 8(3): 9090790913[31]Roy B, Cheung H. A deep learning approach for intrusion detection in Internet of things using bidirectional long shortterm memory recurrent neural network[C] Proc of the 28th Int Telecommunication Networks and Applications Conf. Piscataway, NJ: IEEE, 2018: 16[32]Alkadi O, Moustafa N, Turnbull B, et al. A deep blockchain frameworkenabled collaborative intrusion detection for protecting IoT and cloud networks[J]. IEEE Internet of Things Journal, 2020, 8(12): 94639472[33]Mahdavisharif M, Jamali S, Fotohi R. Big dataaware intrusion detection system in communication networks: A deep learning approach[J]. Journal of Grid Computing, 2021, 19(4): 1946[34]Farahnakian F, Heikkonen J. A deep autoencoder based approach for intrusion detection system[C] Proc of the 20th Int Conf on Advanced Communication Technology (ICACT). Piscataway, NJ: IEEE, 2018: 178183[35]Li X, Chen W, Zhang Q, et al. Building autoencoder intrusion detection system based on random forest feature selection[EBOL]. [20220402]. https:www.sciencedirect.comsciencearticlepiiS0167404820301231[36]Zavrak S, skefiyeli M. Anomalybased intrusion detection from network flow features using variational autoencoder[J]. IEEE Access, 2020, 8(6): 108346108358[37]Yu Y, Long J, Cai Z. Network intrusion detection through stacking dilated convolutional autoencoders[J]. Security and Communication Networks, 2017, 2017(11): 110[38]Gao N, Gao L, Gao Q, et al. An intrusion detection model based on deep belief networks[C] Proc of the 2nd Int Conf Advanced Cloud and Big Data (CBD). Piscataway, NJ: IEEE, 2014: 247252[39]Alrawashdeh K, Purdy C. Toward an online anomaly intrusion detection system based on deep learning[C] Proc of the 15th IEEE Int Conf Machine Learning and Applications (ICMLA). Piscataway, NJ: IEEE, 2015: 195200[40]Chawla S. Deep learning based intrusion detection system for Internet of things[D]. Seattle: University of Washington, 2017[41]Singla A, Bertino E, Verma D. Preparing network intrusion detection deep learning models with minimal data using adversarial domain adaptation[C] Proc of the 15th ACM Asia Conf on Computer and Communications Security. New York: ACM, 2020: 127140[42]Liu X, Li T, Zhang R, et al. A GAN and feature selectionbased oversampling technique for intrusion detection[J]. Security and Communication Networks, 2021, 2021(7): 115[43]肖建平, 龙春, 赵静, 等. 基于深度学习的网络入侵检测研究综述[J]. 数据与计算发展前沿, 2021, 3(3): 5974[44]张小莉, 程光, 张慰慈. 基于改进深度卷积神经网络的网络流量分类方法[J]. 中国科学: 信息科学, 2021, 51(1): 5674[45]Yin C, Zhu Y, Fei J, et al. A deep learning approach for intrusion detection using recurrent neural networks[J]. IEEE Access, 2017, 5(10): 2195421961[46]Xiao Y, Xing C, Zhang T, et al. An intrusion detection model based on feature reduction and convolutional neural networks[J]. IEEE Access, 2019, 7(3): 4221042219[47]Kannari P R, Shariff N C, Biradar R L. Network intrusion detection using sparse autoencoder with SwishPReLU activation model[EBOL]. (20210313) [20220402]. https:link.springer.comarticle10.1007s12652021030770[48]Aldwairi T, Perera D, Novotny M A. An evaluation of the performance of restricted boltzmann machines as a model for anomaly network intrusion detection[J]. Computer Networks, 2018, 144(10): 111119[49]Elsaeidy A, Munasinghe K S, Sharma D, et al. Intrusion detection in smart cities using restricted boltzmann machines[J]. Journal of Network and Computer Applications, 2019, 135(6): 7683[50]Thamilarasu G, Chawla S. Towards deeplearningdriven intrusion detection for the Internet of things[J]. Sensors, 2019, 19(9): 119[51]Lin Z, Shi Y, Xue Z. IDSGAN: Generative adversarial networks for attack generation against intrusion detection[J]. arXiv preprint, arXiv:1809.02077, 2018[52]Liao D, Huang S, Tan Y, et al. Network intrusion detection method based model GAN[C] Proc of the 2020 Int Conf on Computer Communication and Network Security (CCNS). Piscataway, NJ: IEEE, 2020: 153156[53]张昊, 张小雨, 张振友, 等. 基于深度学习的入侵检测模型综述[J]. 计算机工程与应用, 2022, 58(6): 1728[54]彭祯方, 邢国强, 陈兴跃. 人工智能在网络安全领域的应用及技术综述[J]. 信息安全研究, 2022, 8(2): 110116 |
[1] | . [J]. Journal of Information Security Reserach, 2022, 8(8): 793-. |
[2] | . Design and Implementation of Anomaly Detection System for Programmable Data Plane System [J]. Journal of Information Security Reserach, 2022, 8(2): 135-. |
[3] | . Cloud Host Security Management Platform Construction [J]. Journal of Information Security Reserach, 2021, 7(E1): 58-. |
[4] | . Topsec Industrial Intrusion Detection and Audit System [J]. Journal of Information Security Reserach, 2021, 7(E1): 67-. |
[5] | . NetSensor Network Traffic Analysis Solution [J]. Journal of Information Security Reserach, 2021, 7(E1): 126-. |
[6] | . Research on Host Intrusion Fetection Method Based on System Call Behavior Similarity Clustering [J]. Journal of Information Security Reserach, 2021, 7(9): 828-835. |
[7] | . Design of the standard architecture of the network security situation awareness [J]. Journal of Information Security Reserach, 2021, 7(9): 844-848. |
[8] | . Research Progress and Challenge of Advanced Persistent Threat and Its Reconstruction [J]. Journal of Information Security Reserach, 2021, 7(6): 512-519. |
[9] | . Research on Intrusion Detection Model based on Multiple Feature Selection Strategies [J]. Journal of Information Security Research, 2021, 7(3): 225-232. |
[10] | . Phishing Email Analysis of Social Engineering Attacks [J]. Journal of Information Security Research, 2021, 7(2): 166-170. |
[11] | . An Analysis of SDN Attack and Defense Technology [J]. Journal of Information Security Research, 2019, 5(4): 333-339. |
[12] | . Network Intrusion Detection System Model Based on LightGBM [J]. Journal of Information Security Research, 2019, 5(2): 152-156. |
[13] | . Summary of Mobile Payment Security Technology [J]. Journal of Information Security Research, 2019, 5(10): 944-952. |
[14] | . Research of the Scenes in the Cyberspace Game [J]. Journal of Information Security Research, 2018, 4(5): 415-419. |
[15] | . Network Intrusion Detection Method Based on Data Mining [J]. Journal of Information Security Research, 2017, 3(9): 810-816. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||