Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 562-570.doi: 10.11896/jsjkx.210700106

• Information Security • Previous Articles     Next Articles

SMOTE-SDSAE-SVM Based Vehicle CAN Bus Intrusion Detection Algorithm

ZHOU Zhi-hao, CHEN Lei, WU Xiang, QIU Dong-liang, LIANG Guang-sheng, ZENG Fan-qiao   

  1. School of Information and Electrical Engineering,Hunan University of Science and Technology,Xiangtan,Hunan 411201,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:ZHOU Zhi-hao,born in 2000,postgra-duate.His main research interests include information security,machine learning and embedded-development.
    CHEN Lei,born in 1986,Ph.D,lecturer.His main research interests include deep learning,network representation learning,information security of industrial control system and big data analysis.
  • Supported by:
    National Natural Science Foundation of China(62103143),Natural Science Foundation of Hunan Province(2020JJ5199) and National Key Research and Development Program(2019YFE0105300/2019YFE0118700).

Abstract: With the rapid development of in-vehicle equipment intelligence on the Internet of Vehicles,due to its increasingly deepened connection with the Internet,the number of network attacks on the vehicle CAN bus has been increased,the attack methods have become more complex and the attack characteristics have become more concealed.At present,the intrusion detection of the Internet of Vehicles has just started.Traditional detection models based on firewall or rule bases are unable to obtain the hidden deep features of network attacks,but the intelligent detection models based on deep learning present problems such as “over-fitting” or “under-fitting” due to too many training parameters and unbalanced training datasets.To solve the above problems,an SMOTE-SDSAE-SVM based intrusion detection algorithm for CAN bus of vehicles is proposed in this paper,which is simply called 3S.This algorithm tries to combine deep learning and machine learning techniques to extract deep features of network attacks and ensure the efficiency of model training.The main contributions are as follows.Firstly,to balance the training samples of different categories,SMOTE method is used to generate more similar samples through the nearest neighbor sampling strategy.Secondly,sparse autoencoder and denoising autoencoder are combined to increase the speed of feature extraction and eliminate noise effects.And the deep feature of the CAN message is eventually extracted by stacking multi-layer sparse denoising autoencoder.Finally,SVM is used to accurately classify the extracted deep features of CAN messages,thereby discovering network attacks.According to the extensive experiments on the Volvo CAN dataset and the CAR-HACKING dataset,the proposed 3S algorithm is proved to have better accuracy and lower false alarm rate than other algorithms.

Key words: CAN bus, Deep learning, Intrusion detection, SDSAE, SMOTE, SVM

CLC Number: 

  • TP183
[1] QIAN Z H,WANG Y J.IoT technology and application[J].Acta Electronica Sinica,2012,40(5):1023-1029.
[2] QIE G,ZHANG Y.Intelligent Connected Vehicle:A Survey of the Technical Analysis[J].Mobile Communications,2020,44(1):80-85.
[3] LI K Q,DAI Y F,LI S B,et al.State-of-the-art and technical trends of intelligent and connected vehicles[J].Journal of Automotive Safety and Energy,2017,8(1):1-14.
[4] XUN Y J,LIU J J,ZHAO J.Research on security threat of intelligent connected vehicle[J].Chinese Journal on Internet of Things,2019,3(4):72-81.
[5] YANG D,JIANG K,ZHAO D,et al.Intelligent and connected vehicles:Current status and future perspectives[J].Science China-Technological Sciences,2018,61(10):1446-1471.
[6] ALNABULSI H,ISLAM R.Protecting code injection attacks in intelligent transportation system[C]//Trust Security and Privacy in Computing and Communications.Piscataway:IEEE Press,2019:799-806.
[7] HAO J,HAN G.On the Modeling of Automotive Security:ASurvey of Methods and Perspectives[J].Future Internet,2020,12(11):198.
[8] MÜTER M,GROLL A,FREILING F C.A structured approach to anomaly detection for in-vehicle networks[C]//2010 Sixth International Conference on Information Assurance and Security(IAS).IEEE,2010:92-98.
[9] HAN J,PEI J,KAMBER M.Data mining:concepts and techniques[M].San Francisco:Elsevier,2011.
[10] GUO T,XU Z,YAO X,et al.Robust online time series prediction with recurrent neural networks[C]//2016 IEEE International Conference on Data Science and Advanced Analytics(DSAA).IEEE,2016:816-825.
[11] TAYLOR A,LEBLANC S,JAPKOWICZ N.Anomaly detection in automobile control network data with long short-term memory networks[C]//2016 IEEE International Conference on Data Science and Advanced Analytics(DSAA).IEEE,2016:130-139.
[12] LIANG J,CHEN J,ZHANG X,et al.One-hot encoding and convolutional neural network based anomaly detection[J].Journal Tsinghua University(Science & Technology),2019,59(7):523-529.
[13] ZHU F,WU W,FU Y C,et al.A Dual Deep Network Based Secure Deep Reinforcement Learning Method[J].Chinese Journal of Computers,2019,42(8):1-15.
[14] AN T L,WANG C D,YANG C.Research on vehicle bus anomaly detection based on LSTM[J].Journal of Tianjin University of Technology,2020,36(3):6-10.
[15] LIU X Q,SHAN C,REN J D,et al.An intrusion detection method based on multi-dimensional optimization of traffic anomaly analysis[J].Netinfo Security,2019,4(1):14-26.
[16] LI L J,YU Y,BAI S S,et al.Intrusion Detection Model Based on Double Training Technique[J].Transactions of Beijing Institute of Technology,2017(12):1246-1252.
[17] CHAWALA N V,BOWYER K W,HALL L O,et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artificial Intelligence Research,2002,16(1):321-357.
[18] LIN Y.Research on fusion algorithm of extreme learning machine and auto-encoder [D].Changchun:Jilin University,2016.
[19] PARK S,SEO S,KIM J.Network intrusion detection usingstacked denoising autoencoder[J].Advanced Science Letters,2017,23(10):9907-9911.
[20] VINCENT P,LATOCHELLE H,LAJOIE I,et al.Stacked denoising auto-encoders:learning useful representations in a deep network with a local denoising criterion[J].The Journal of Machine Learning Research,2010,11(12):3371-3408.
[21] SUN W J,SHAO S Y,ZHAO R,et al.A sparse auto-encoder-based deep neural network approach for induction motor faults classification[J].Measurement,2016,89:171-178.
[22] XING C,MA L,YANG X Q.Stacked denoise autoencoder based feature extraction and classification for hyperspectral images[J].Journal of Sensors,2016,2016:1-10.
[23] ANDREAS T.Anomaly detection in recordings from in-vehicle networks[J].Big Data and Applications,2014(3):23-29.
[24] CRISTIANINI N,TAYLOR J S.An introduction to supportvector machines and other kernel-based learning methods[M].Beijing:Publishing House of Electronics Industry,2004.
[25] AMARNATH B,BALAMURUGAN S A A.Review on feature se-lection techniques and its impact for effective data classification using UCI machine learning repository dataset[J].Journal of Engineering Science and Technology,2016,11(11):1639-1646.
[26] MOUSTAFA N,SLAY J.UNSW-NB15:a comprehensive data set for network intrusion detection systems(UNSW-NB15 network data set)[C]//Proceedings of the 2015 Military Communications and Information Systems Conference.Canberra,ACT,Australia:IEEE,2015:1-6.
[27] GOH J,ADEPU S,JUNEJO K N,et al.A dataset to support research in the design of secure water treatment systems[C]//International Conference on Critical Information Infrastructures Security.Cham:Springer,2016:88-99.
[28] AHMED C M,PALLETI V R,MATHUR A P.WADI:a water distribution testbed for research in the design of secure cyber physical systems[C]//Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks.2017:25-28.
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