Network Intrusion Detection Based on Spatial Features and GenerativeAdversarial Networks

NAVIGATE

Table of Contents

STATISTICS

Viewed424

Downloads394

Network Intrusion Detection Based on Spatial Features and GenerativeAdversarial Networks

[HTML] PDF下载 (394)

[1]ZHANG Zhen,ZHOU Yicheng,TIAN Hongpeng.Network Intrusion Detection Based on Spatial Features and GenerativeAdversarial Networks[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):40-47.[doi:10.13705/j.issn.1671-6833.2024.06.001]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 06 Page number: 40-47 Column: Public date: 2024-09-25

Title:: Network Intrusion Detection Based on Spatial Features and GenerativeAdversarial Networks

Author(s):: ZHANG Zhen¹; ZHOU Yicheng²; TIAN Hongpeng¹; 1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, China

Keywords:: intrusion detection; anomaly detection; generative adversarial networks; image encoding; convolutionalneural networks

CLC:: TP393

DOI:: 10.13705/j.issn.1671-6833.2024.06.001

Abstract:: Address issues such as the inadequate consideration of inter-feature correlations in existing intrusion detection methods and the need for improved detection accuracy on high-dimensional discrete datasets, a network intrusion detection method MBGAN based on spatial features and generative adversarial networks was proposed. Initially, a transformation approach was devised to convert one-dimensional data into two-dimensional grayscale images, enabling convolutional kernels to capture richer contextual information. Subsequently, a bidirectional generative adversarial network model was employed for anomaly detection. The model was trained using network traffic images, incorporating the minimum Wasserstein distance and gradient penalty techniques to mitigate mode collapseand instability during generative adversarial network training. Experimental verification showed that the detection accuracy of the proposed method on the NSL-KDD, UNSW-NB15 and CICIDIS2017 datasets was 97. 4%, 92. 3% and94. 8%, the recall rates were 97. 2%, 93. 1% and 95. 6%, and the F1 were 97. 3%, 93. 0% and 95. 2%, respectively, which were better than those of other methods.

References:: [1] AHMED M, MAHMOOD A N, HU J K. A survey of network anomaly detection techniques [ J] . Journal of Network and Computer Applications, 2016, 60(C) : 19-31.
[2] FERRAG M A, MAGLARAS L, MOSCHOYIANNIS S,et al. Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study [ J] .Journal of Information Security and Applications, 2020,50(C) :102419.
[3] ZHANG X Q, YANG F, HU Y, et al. RANet: networkintrusion detection with group-gating convolutional neuralnetwork[ J] . Journal of Network and Computer Applications, 2022, 198: 103266.
[4] AL-HAWAWREH M, MOUSTAFA N, GARG S, et al.Deep learning-enabled threat intelligence scheme in theinternet of things networks [ J ] . IEEE Transactions onNetwork Science and Engineering, 2021, 8( 4) : 2968 -2981.
[5] 张安琳, 张启坤, 黄道颖, 等. 基于 CNN 与 BiGRU 融合神经网络的入侵检测模型[ J] . 郑州大学学报( 工学版) , 2022, 43(3) : 37-43.
ZHANG A L, ZHANG Q K, HUANG D Y, et al. Intrusion detection model based on CNN and BiGRU fusedneural network [ J ] . Journal of Zhengzhou University(Engineering Science) , 2022, 43(3) : 37-43.
[6] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, etal. Generative adversarial networks[EB / OL]. (2014-06-10) [2024-01-10] . http:∥arxiv. org / abs/ 1406. 2661.
[7] DIVYA S, JIANNONG C. Generative adversarial networks (GANs) : challenges, solutions, and future directions[ J] . ACM Computing Surveys, 2022,54(3) :1-42.
[8] ZHOU N R, ZHANG T F, XIE X W, et al. Hybrid quantum-classical generative adversarial networks for imagegeneration via learning discrete distribution [ J ]. SignalProcessing: Image Communication, 2023, 110: 116891.
[9] FRID-ADAR M, KLANG E, AMITAI M, et al. Synthetic data augmentation using GAN for improved liver lesionclassification[C]∥2018 IEEE 15th International Symposium on Biomedical Imaging ( ISBI 2018 ) . Piscataway:IEEE, 2018: 289-293.
[10] ISOLA P, ZHU J Y, ZHOU T H, et al. Image-to-imagetranslation with conditional adversarial networks [ C ] ∥2017 IEEE Conference on Computer Vision and PatternRecognition ( CVPR) . Piscataway: IEEE, 2017: 5967 -5976.
[11] ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]∥2017 IEEE International Conference on Computer Vision ( ICCV) . Piscataway: IEEE, 2017: 2242 -2251.
[12] SCHLEGL T, SEEBÖCK P, WALDSTEIN S M, et al.Unsupervised anomaly detection with generative adversarial networks to guide marker discovery [EB / OL]. ( 2017 -03-17)[2024-01-10]. http:∥arxiv. org / abs/ 1703. 05921.
[13] LI D, CHEN D C, JIN B H, et al. MAD-GAN: multivariate anomaly detection for time series data with generativeadversarial networks[C]∥Artificial Neural Networks andMachine Learning-ICANN 2019. New York:ACM, 2019:703-716.
[14] DONAHUE J, KRÄHENBÜHL P, DARRELL T. Adversarial feature learning[EB / OL] . (2017-04-03) [2024-01-10] . http:∥arxiv. org / abs/ 1605. 09782.
[15] GEIGER A, LIU D Y, ALNEGHEIMISH S, et al.TadGAN: time series anomaly detection using generativeadversarial networks[ C]∥2020 IEEE International Conference on Big Data. Piscataway: IEEE, 2020: 33-43.
[16] 刘拥民, 杨钰津, 罗皓懿, 等. 基于双向循环生成对抗网络的无线传感网入侵检测方法[ J] . 计算机应用,2023, 43(1) : 160-168.
LIU Y M, YANG Y J, LUO H Y, et al. Intrusion detection method for wireless sensor network based on bidirectional circulation generative adversarial network[ J] . Journal of Computer Applications, 2023, 43(1): 160-168.
[17] 胡梦娜, 何强, 贾俊铖, 等. EB-GAN: 基于 BiGAN 的网络流量异常检测方法 [ J] . 计算机应用与软件,2023, 40(6) : 303-309.
HU M N, HE Q, JIA J C, et al. EB-GAN: network traffic anomaly detection method based on BiGAN[ J] . Computer Applications and Software, 2023, 40 ( 6 ) : 303-309.
[18] SONG J Y, PAUL R, YUN J H, et al. CNN-based anomaly detection for packet payloads of industrial controlsystem [ J ] . International Journal of Sensor Networks,2021, 36(1) : 36-49.
[19] ANDRESINI G, APPICE A, MALERBA D. Nearestcluster-based intrusion detection through convolutionalneural networks [ J] . Knowledge-Based Systems, 2021,216: 106798.
[20] LI Z P, QIN Z, HUANG K, etal. Intrusion detection using convolutional neural networks for representationlearning[C]∥Neural Information Processing: 24th International Conference. New York:ACM, 2017: 858-866.
[21] KIM T, SUH S C, KIM H, et al. An encoding techniquefor CNN-based network anomaly detection [ C ] ∥2018IEEE International Conference on Big Data. Piscataway:IEEE, 2018: 2960-2965.
[22] VAN DER MAATEN L, HINTON G. Viualizing data using T-SNE[ J] . Journal of Machine Learning Research,2008,9(2605) : 2579-2605.
[23] PREPARATA F P, HONG S J. Convex hulls of finitets of points in two and three dimensions[ J] . Communications of the ACM, 1977, 20(2) : 87-93.
[24] VERGARA J R, ESTÉVEZ P A. A review of feature selection methods based on mutual information[ J] . NeuralComputing and Applications, 2014, 24(1) : 175-186.
[25] RUBNER Y, TOMASI C, GUIBAS L J. The earth mover′sdistance as a metric for image retrieval[ J] . InternationalJournal of Computer Vision, 2000,40(2) :99-121.
[26] GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[ C]∥Proceedings ofthe 31st International Conference on Neural InformationProcessing Systems. New York:ACM, 2017: 5769-5779.
[27] LICHMAN M. UCI machine learning repository [ EB /OL] . [ 2024 - 01 - 10 ] . https:∥www. unb. ca / cic / datasets/ nsl. html.
[28] MOUSTAFA N,SLAY J. UNSW-NB15: a comprehensivedataset for network intrusion detection systems[ C]∥ Proceedings of the 2015 Military Communicationsand Information Systems Conference. Piscataway: IEEE,2015:1-6.
[29] IMAN S, ARASH H, Ali G. Toward Generating a NewIntrusion Detection Dataset and Intrusion Traffic Characterization[EB / OL] . [2024-01-10] . https:∥specialsci.cn / detail / 4ff953c0- 6952 - 4916 - bc7d - 7c4d851f868e?resourceType = 0.
[30] ZHAI S F, CHENG Y, LU W N, et al. Deep structuredenergy based models for anomaly detection[C]∥Proceedings of the 33rd International Conference on InternationalConferenceon Machine Learning-Volume 48. New York:ACM, 2016: 1100-1109.
[31] ZENATI H, ROMAIN M, FOO C S, et al. Adversariallylearned anomaly detection[ C]∥2018 IEEE InternationalConference on Data Mining ( ICDM) . Piscataway:IEEE,2018: 727-736.

Similar References:

Memo

Last Update: 2024-09-29