Computer Science ›› 2022, Vol. 49 ›› Issue (3): 144-151.doi: 10.11896/jsjkx.210100142

• Database & Big Data & Data Science • Previous Articles     Next Articles

Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder

WU Yu-kun, LI Wei, NI Min-ya, XU Zhi-cheng   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2021-01-18 Revised:2021-05-01 Online:2022-03-15 Published:2022-03-15
  • About author:WU Yu-kun,born in 1980,Ph.D student,is a member of China Compu-ter Federation.His main research in-terests include machine learning and big data.
    LI Wei,born in 1958,Ph.D,professor.His main research interests include big data,block chain,IOT,and smart city development.
  • Supported by:
    National Natural Science Foundation of China(61502422,61972056),Natural Science Foundation of Zhejiang Province,China(LY18F020028),Public Welfare Project of Zhejiang Science and Technology Department(2017C33108) and General Research Project of Zhejiang Provincial Department of Education(Y202044619).

Abstract: Large-scale high-dimensional unbalanced data handling is a major challenge in anomaly detection.One-class support vector machine(OCSVM) is very efficient at handling unbalanced data,but it is not suitable for large-scale high-dimensional dataset.Meanwhile,the kernel function of OCSVM also has an important influence on the detection performance.An anomaly detection model combining a deep auto-encoder and a one-class support vector machine is proposed.The deep auto-encoder is not only responsible for extracting features and dimensionality reduction,but also mapping an adaptive kernel function.As a whole,the model adopts the gradient descent method to carry out joint training and realizes end-to-end training.Experiment is conducted on four public datasets and compared with other anomaly detection methods.Experimental results show that the proposed model has better performance than single-kernel or multi-kernel one-class support vector machines and other models in terms of AUC and RECALL,and the proposed model is robust at different anomaly rate and has great advantages in time complexity.

Key words: Anomaly detection, Deep auto-encoder, Hybrid model, One-class SVM

CLC Number: 

  • TP391
[1]TIAN Y J,MIRZABAGHERI M,BAMAKAN S M H,et al.Ramp loss one-class support vector machine:A robust and effective approach to anomaly detection problems[J].Neurocompu-ting,2018,310(6):223-235.
[2]CHANDOLA V,BANERJEE A,KUMAR V,et al.Anomalydetection:A survey[J].ACM Computing Surveys,2009,41(3):15:1-15:58.
[3]YE Q,YANG J,YIN T,et al.Can the virtual labels obtained by traditional LP approaches be well encoded in WLR[J].IEEE Transactions on Neural Networks and Learning Systems,2016,27(7):1591-1598.
[4]CANDES E J,LI X D,MA Y,et al.Robust principal component analysis[J].arXiv:0912,3599,2009.
[5]CHALAPATHY R,MENON A K,CHAWLA S,et al.Robust,deep and inductive anomaly detection[C]//Proceedings of Machine Learning and Knowledge Discovery in Databases.Skopje:Macedonia,2017:36-51.
[6]SCHÖLKOPF B,PLATT J C,TAYLOR J,et al.Estimating the support of a high-dimensional distribution[J].Neural Computation,2001,13 (7):1443-1471.
[7]TAX D M,DUIN R P.Support vector data description[J].Machine Learning,2004,54(1):45-66.
[8]LIU F T,TING K M,ZHOU Z H,et al.Isolation forest[C]//Proceedings of 2008 Eighth IEEE International Conference on Data Mining.Pisa,Italy:IEEE Computer Society,2008:413-422.
[9]KIM J,SCOTT C D.Robust kernel density estimation[J].Journal of Machine Learning Research,2012,13(9):2529-2565.
[10]ZIMEK A,SCHUBERT E,KRIEGEL H P,et al.A survey on unsupervised outlier detection in high-dimensional numerical data[J].Statistical Analysis and Data Mining,2012,5(5):363-387.
[11]GAUTAM C,BALAJI R,SUDHARSAN K,et al.LocalizedMultiple Kernel learning for Anomaly Detection:One-class Classification[J].Knowledge-Based Systems,2019,165(1):241-252.
[12]LI H Q,YING N,GUO C S,et al.High-dimensional outlier detection based on deep belief network and linear one-class SVM[J].Telecommunication Science,2018,34(1):34-42.
[13]JIN P,XIA X F,QIAO Y,et al.High-Dimensional Data Anomaly Detection for WSNs Based on Deep Belief Network[J].Chinese Journal of Sensors and Actuators,2019,32(6):892-901.
[14]ERFANI S M,RAJASEGARAR S,KARUNASEKERA S,et al.High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J].Pattern Recognition,2016,58(10):121-134.
[15]ZHOU C,PAFFENROTH R C.Anomaly detection with robust deep autoencoders[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,KDD17.New York:ACM,2017:665-674.
[16]XIE J Y,GIRSHICK R,FARHADI A,et al.Unsupervised deep embedding for clustering analysis[C]//Proceedings of the 33rd International Conference on Machine Learning.New York:JMLR.org,2016:478-487.
[17]GEHLER P V,NOWOZIN S.Infinite Kernel Learning[R].Tübingen,Max Planck Institute for Biological Cybernetics Technical Report,2008.
[18]BENGIO Y,LAMBLIN P,POPOVICI D,et al.Greedy layer-wise training of deep networks[C]//Proceedings of Advances in Neural Information Processing Systems 19(NIPS).Vancouver,BC,Canada:MIT Press,2007:153-160.
[19]CHALAPATHY R,MENON A K,CHAWLA S,et al.Anomaly detection using one-class neural networks[J].arXiv:1802.06360,2019.
[20]REVATHI S,MALATHI A.A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection[J].International Journal of Engineering Research & Technology,2013,2(11):1848-1853.
[21]MOUSTAFA N,SLAY J.The evaluation of Network Anomaly Detection Systems:Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set[J].Information Systems Security,2016,25(1/2/3):18-31.
[22]SHARAFALDIN I,LASHKARI A H,GHORBANI A A,et al.Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization[C]//Proceedings of the 4th International Conference on Information Systems Security and Privacy.2018:108-116.
[23]PING R,ZHOU S S,LI D,et al.Cost Sensitive Random Forest Classification Algorithm for Highly Unbalanced Data[J].Pattern Recognition and Artificial Intelligence,2020,33(3):249-257.
[24]ALI R,BENJAMIN R.Random features for large-scale kernelmachines[C]//Proceedings of Advances in Neural Information Processing Systems 20.Vancouver,British Columbia,Canada:Curran Associates Inc,2008:1177-1184.
[25]SHARAFALDIN I,GHARIB A,LASHKARI A H,et al.Towards a reliable intrusion detection benchmark dataset[J].Software Networking,2018,2017(1):177-200.
[26]FAN J N,ZHANG Q R,ZHU J L,et al.Robust deep auto-encoding Gaussian process regression for unsupervised anomaly detection[J].Neurocomputing,2020,376(1):180-190.
[27]XAVIER G,BENGIO Y.Understanding the diffıculty of training deep feedforward neural networks[C]//Proceedings of the Thirteenth International Conference on Artifıcial Intelligence and Statistics,Proceedings of Machine Learning Research.Chia Laguna Resort,Sardinia,Italy:Proceedings.mlr.press,2010:249-256.
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