Computer Science ›› 2022, Vol. 49 ›› Issue (3): 144-151.doi: 10.11896/jsjkx.210100142
• Database & Big Data & Data Science • Previous Articles Next Articles
WU Yu-kun, LI Wei, NI Min-ya, XU Zhi-cheng
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[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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