Computer Science ›› 2019, Vol. 46 ›› Issue (3): 197-201.doi: 10.11896/j.issn.1002-137X.2019.03.029

• Information Security • Previous Articles     Next Articles

Intrusion Detection Based on Semi-supervised Learning with Deep Generative Models

CAO Wei-dong, XU Zhi-xiang, WANG Jing   

  1. College of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300,China
  • Received:2018-01-18 Revised:2018-05-23 Online:2019-03-15 Published:2019-03-22

Abstract: Aiming at the difficulties that training samples of intrusion detection algorithms based on supervised learning are insufficient,and unsupervised algorithms have low detection rate,a new semi-supervised intrusion detection method based on deep generative models was proposed.This method aims to improve the detection accuracy and the generalization ability of the model by constructing an effective objective function.First,variational auto-encoder in the model is employedto map the vector of raw data from the high-dimensional space to low-dimensional,and the corresponding optimal low-dimension representation of raw can be obtained.Then,the generative model is used to improve the classification accuracy by only using the labeled samples.Experiments show that this method can achieve high accuracy while using a limited number of labeled samples.

Key words: Generative model, Intrusion detection, Semi-supervised, Variational autoencoder

CLC Number: 

  • TP393.08
[1]CHANDOLA V,BANERJEE A,KUMAR V.Anomaly detec-
tion:A survey[J].ACM Computing Surveys(CSUR),2009,41(3):1-58.
[2]DENNING D E.An Intrusion-Detection Model.IEEE Transactions on Software Engineering,2006,SE-13(2):222-232.
[3]SOMMER R,PAXSON V.Outside the Closed World:On Using Machine Learning for Network Intrusion Detection[C]∥IEEE Symposium on Security and Privacy.IEEE Computer Society,2010:305-316.
[4]LASKOV P,DSSEL P,SCHFER C,et al.Learning Intrusion Detection:Supervised or Unsupervised?[C]∥International Conference on Image Analysis and Processing.Springer-Verlag,2005:50-57.
[5]LIANG C,LI C H.Novel Intrusion Detection Method Based on Semi-supervised Clustering[J].Computer Science,2016,43(5):87-90.(in Chinese)
梁辰,李成海.一种新的半监督入侵检测方法[J].计算机科学,2016,43(5):87-90.
[6]YANG S L,YANG Y H,SHEN Q N,et al.A method of Intrusion Detection Based on Semi-Supervised GHSOM[J].Journal of Computer Research and Development,2013,50(11):2375-2382.(in Chinese)
阳时来,杨雅辉,沈晴霓,等.一种基于半监督GHSOM的入侵检测方法[J].计算机研究与发展,2013,50(11):2375-2382.
[7]ZHANG X,ZHU P,TIAN J,et al.An effective semi-supervised model for intrusion detection using feature selection based Lap-SVM[C]∥2017 International Conference on Computer,Information and Telecommunication Systems (CITS).Dalian,2017:283-286.
[8]ASHFAQ R A R,WANG X Z,HUANG J Z,et al.Fuzziness based semi-supervised learning approach for intrusion detection system[J].Information Sciences An International Journal,2017,378(C):484-497.
[9]NOSADA G,OMOTE K,NISHIDE T.Network Intrusion Detection Based on Semi-supervised Variational Auto-Encoder[C]∥European Symposium on Research in Computer Security-ESORICS 2017.Cham:Springer,2017.
[10]FITRIANI S,MANDALA S,MURTI M A.Review of semi-supervised method for Intrusion Detection System[C]∥Multimedia and Broadcasting.IEEE,2017:36-41.
[11]KINGMA D P,WELLING M.Auto-Encoding Variational Bayes[C]∥Conference proceedings:papers accepted to the International Conference on Learning Representations (ICLR).2014.
[12]KINGMA D P,REZENDE D J,MOHAMED S,et al.Semi-Supervised Learning with Deep Generative Models[J].Advances in Neural Information Processing Systems,2014,4:3581-3589.
[13]MERZ C J,CLAIR D C,BOND W E.SeMi-supervised adaptive resonance theory (SMART2)[C]∥International Joint Con-ference on Neural Networks.IEEE,1992.
[14]周志华.机器学习[M].北京:清华大学出版社,2016:298-297.
[15]LIU J W,LIU Y,LUO X L.Semi-Supervised Learning Methods[J].Chinese Journal of Couputers,2015,38(8):1592-1617.(in Chinese)
刘建伟,刘媛,罗雄麟.半监督学习方法[J].计算机学报,2015,38(8):1592-1617.
[16]GAO N,GAO L,HE Y Y,et al.A Lightweight Intrusion Detection Model Based on Autoencoder Network with Feature Reduction[J].2017,45(3):730-739.(in Chinese)
高妮,高岭,贺毅岳,等.基于自编码网络特征降维的轻量级入侵检测模型[J].电子学报,2017,45(3):730-739.
[17]TAVALLAEE M,BAGHERI E,LU W,et al.A detailed analysis of the KDD CUP 99 data set[C]∥IEEE International Conference on Computational Intelligence for Security & Defense Applications.IEEE,2009:1-6.
[1] WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan. Collaborative Filtering Recommendation Method Based on Vector Quantization Coding [J]. Computer Science, 2022, 49(9): 48-54.
[2] WANG Xin-tong, WANG Xuan, SUN Zhi-xin. Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network [J]. Computer Science, 2022, 49(8): 314-322.
[3] WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25.
[4] HOU Xia-ye, CHEN Hai-yan, ZHANG Bing, YUAN Li-gang, JIA Yi-zhen. Active Metric Learning Based on Support Vector Machines [J]. Computer Science, 2022, 49(6A): 113-118.
[5] ZHOU Zhi-hao, CHEN Lei, WU Xiang, QIU Dong-liang, LIANG Guang-sheng, ZENG Fan-qiao. SMOTE-SDSAE-SVM Based Vehicle CAN Bus Intrusion Detection Algorithm [J]. Computer Science, 2022, 49(6A): 562-570.
[6] CAO Yang-chen, ZHU Guo-sheng, SUN Wen-he, WU Shan-chao. Study on Key Technologies of Unknown Network Attack Identification [J]. Computer Science, 2022, 49(6A): 581-587.
[7] WEI Hui, CHEN Ze-mao, ZHANG Li-qiang. Anomaly Detection Framework of System Call Trace Based on Sequence and Frequency Patterns [J]. Computer Science, 2022, 49(6): 350-355.
[8] WANG Yu-fei, CHEN Wen. Tri-training Algorithm Based on DECORATE Ensemble Learning and Credibility Assessment [J]. Computer Science, 2022, 49(6): 127-133.
[9] XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo. Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [J]. Computer Science, 2022, 49(3): 288-293.
[10] QIAO Jie, CAI Rui-chu, HAO Zhi-feng. Mining Causality via Information Bottleneck [J]. Computer Science, 2022, 49(2): 198-203.
[11] HOU Hong-xu, SUN Shuo, WU Nier. Survey of Mongolian-Chinese Neural Machine Translation [J]. Computer Science, 2022, 49(1): 31-40.
[12] ZHANG Shi-peng, LI Yong-zhong. Intrusion Detection Method Based on Denoising Autoencoder and Three-way Decisions [J]. Computer Science, 2021, 48(9): 345-351.
[13] LI Bei-bei, SONG Jia-rui, DU Qing-yun, HE Jun-jiang. DRL-IDS:Deep Reinforcement Learning Based Intrusion Detection System for Industrial Internet of Things [J]. Computer Science, 2021, 48(7): 47-54.
[14] CHENG Xi, CAO Xiao-mei. SQL Injection Attack Detection Method Based on Information Carrying [J]. Computer Science, 2021, 48(7): 70-76.
[15] ZHAO Min, LIU Jing-lei. Semi-supervised Clustering Based on Gaussian Fields and Adaptive Graph Regularization [J]. Computer Science, 2021, 48(7): 137-144.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!