Computer Science ›› 2021, Vol. 48 ›› Issue (9): 345-351.doi: 10.11896/jsjkx.200500059

• Information Security • Previous Articles    

Intrusion Detection Method Based on Denoising Autoencoder and Three-way Decisions

ZHANG Shi-peng, LI Yong-zhong   

  1. School of Computer,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China
  • Received:2020-05-14 Revised:2020-08-21 Online:2021-09-15 Published:2021-09-10
  • About author:ZHANG Shi-peng,born in 1994,postgraduate.His main research intrests include computer network security and machine learning.
    LI Yong-zhong,born in 1961,M.S,professor,M.S supervisor.His main research interests include computer network security and information security,intelligent information processing,and application of embedded system.
  • Supported by:
    National Nature Science Foundation of China(61471182),Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX20_3163) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China(15KJD52004)

Abstract: Intrusion detection plays a vital role in computer network security.Intrusion detection is one of the key technologies of network security and needs to be kept under constant attention.As the network environment becomes more and more complex,network intrusion behaviors gradually show diversified and intelligent characteristics,and network intrusion is also becoming more difficult to detect.And the research conducted in the field of network security is also an endless study.For the above reasons,people are worried about the feasibility and sustainability of the current method,specifically,it is difficult for current intrusion detection methods to perfectly abstract the features contained in intrusion behaviors,and most of the current intrusion detection methods perform poorly on unknown attacks.In response to these problems,we propose an intrusion detection method DAE-3WD based on denoising autoencoder and three-way decisions.We hope that our method can effectively promote the research on intrusion detection.This proposed methodextracts features from high-dimensional data through denoising autoencoder.Through multiple feature extractions,a multi-granular feature space can be constructed,and then an immediate decision on intrusive or no-rmal behavior is made based on the three-way decisions,and further analysis is required for suspected intrusion or normal beha-vior.Deep learning has superior hierarchical feature learning ability,and three-way decisions can avoid the risk of blind classification due to insufficient information.This method uses these characteristics to achieve the purpose of improving the performance of intrusion detection.The NSL-KDD data set is used in our experiments.The experiments prove that the proposed method can extract meaningful features and effectively improve the performance of intrusion detection.

Key words: Autoencoder, Feature extraction, Intrusion detection, Network security, Three-way decisions

CLC Number: 

  • TP309
[1]GAO N,GAO L,GAO Q,et al.An Intrusion Detection Model Based on Deep Belief Networks[C]//2014 Second International Conference on Advanced Cloud and Big Data.IEEE,2014:247-252.
[2]QIAN Y Y,LI Y Z,YU X Y.Intrusion Detection Method Based on Multi-label and Semi-Supervised Learning[J].Computer Science,2015,42(2):134-136.
[3]NESPOLI P,PAPAMARTZIVANOS D,MÁRMOL F G,et al.Optimal Countermeasures Selection Against Cyber Attacks:A Comprehensive Survey on Reaction Frameworks[J].IEEE Communications Surveys & Tutorials,2017,20(2):1361-1396.
[4]DÍAZ-LÓPEZ D,DÓLERA-TORMO G,GÓMEZ-MÁRMOL F,et al.Dynamic Counter-Measures for Risk-Based Access Control Systems:An Evolutive Approach[J].Future Generation Computer Systems,2016,55:321-335.
[5]LU Y.Research on a New Hybrid Intrusion Detection Algo-rithm for Cloud Computing[J].Journal of Chongqing University of Technology (Natural Science),2020,34(10):153-159.
[6]LI Y Z,ZHANG J.Intrusion Detection Algorithm Based onCluster and Cloud Model[J].Computer Science,2015(2):33.
[7]GAO L Y,TIAN Z S,LI L X,et al.A SVDD-Based Method for WLAN Indoor Passive Intrusion[J].Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition),2020,32(2):200-209.
[8]ZHANG Y S,JIANG S Y.Research on Network Intrusion Detection Based on Rick Data Mining Tracking Technology[J].Journal of Chongqing University of Technology (Natural Scien-ce),2019,33(10):127-135.
[9]HINTON G E,OSINDERO S,TEH Y W.A Fast Learning Algorithm For Deep Belief Nets[J].Neural Computation,2006,18(7):1527-1554.
[10]WEI P,LI Y,ZHANG Z,et al.An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network[J].IEEE Access,2019,7:87593-87605.
[11]YANG Y Q,ZHENG K F,WU B,et al.Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder with Regularization[J].IEEE Access,2020,8:42169-42184.
[12]LI Y Z,ZHANG S P,LI Y,et al.Research on Intrusion Detection Algorithm Based on Deep Learning and Semi-Supervised Clustering[J].International Journal of Cyber Research and Education (IJCRE),2020,2(2):38-60.
[13]VINCENT P,LAROCHELLE H,BENGIO Y,et al.Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the 25th International Conference on Machine Learning.ACM,2008:1096-1103.
[14]YAO Y Y.Three-way decision:an interpretation of rules inrough set theory[C]//International Conference on Rough Sets and Knowledge Technology.Berlin,Heidelberg:Springer,2009:642-649.
[15]ZHANG Y B,MIAO D Q,ZHANG Z F.Multi-granularity text sentiment classification model based on three-way decisions[J].Computer Science,2017,44(12):188-193.
[16]ZHANG L B,LI H X,ZHOU X Z,et al.Sequential three-way decision based on multi-granular autoencoder features[J].Information Sciences,2020,507:630-643.
[17]LIU D,LIANG D C.Generalized three-way decisions and special three-way decisions[J].Journal of Frontiers of Computer Scien-ce and Technology,2017,11(3):502-510.
[18]MALDONADO S,PETERS G,WEBER R.Credit scoringusing three-way decisions with probabilistic rough sets[J].Information Sciences,2020,507:700-714.
[19]YAO Y Y.Granular computing and sequential three-way decisions[C]//International Conference on Rough Sets and Knowle-dge Technology.Berlin,Heidelberg:Springer,2013:16-27.
[20]SENTHILNAYAKI B,VENKATALAKSHMI K,KANNANA.Intrusion Detection System using Fuzzy Rough Set Feature Selection and Modified KNN Classifier[J].International Arab Journal of Information Technology,2019,16(4):746-753.
[21]AL-QATF M,LASHENG Y,AL-HABIB M,et al.Deep lear-ning approach combining sparse autoencoder with SVM for network intrusion detection[J].IEEE Access,2018,6:52843-52856.
[22]REN J D,LIU X Q,WANG Q,et al.A Multi-Level Intrusion Detection Method Based on KNN Outlier Detection and Random Forests[J].Journal of Computer Research and Development,2019,56(3):566-575.
[23]DING Y,LI Y Z.Research on Intrusion Detection Algorithm Based on PCA and Semi-Supervised Clustering[J].Journal of Shandong University (Engineering Science),2012,42(5):41-46.
[24]DU Y,ZHANG Y D,LI M H,et al.Improved Fast ICA algorithm for data optimization processing in intrusion detection[J].Journal on Communications,2016,37(1):42-48.
[25]GHASEMI J,ESMAILY J.Intrusion Detection Systems Using a Hybrid SVD-Based Feature Extraction Method[J].International Journal of Security and Networks,2017,12(4):230-240.
[26]LIU J H,MAO S P,FU X M.Intrusion Detection Model Based on ICA Algorithm and Deep Neural Network[J].NetinfoSecu-rity,2019,19(3):1-10.
[27]FENG W Y,GUO X B,HE Y Y,et al.Intrusion DetectionModel Based on Feedforward Neural Network[J].Netinfo Security,2019,19(9):101-105.
[28]SHONE N,NGOC T N,PHAI V D,et al.A Deep Learning Approach to Network Intrusion Detection[J].IEEE Transactions on Emerging Topics in Computational Intelligence,2018,2(1):41-50.
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