Computer Science ›› 2024, Vol. 51 ›› Issue (8): 20-33.doi: 10.11896/jsjkx.230600052
• Database & Big Data & Data Science • Previous Articles Next Articles
KONG Lingchao, LIU Guozhu
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| [1]HAWKINS D M.Identification of outliers[M].Vol.11.Lon-don:Chapman and Hall,1980. [2]WANG H,BAH M J,HAMMAD M.Progress in outlier detection techniques:A survey[J].IEEE Access 7(2019):107964-108000. [3]JIANG F,WANG K L,YU X,et al.Summary of Intrusion Detection Models Based on Deep Learning[J].Control and Decision,2020,35(5):1199-1204. [4]ZHANG W A,HONG Z,ZHU J W,et al.A survey of network intrusion detection methods for industrial control systems[J].Control and Decision,2019,34(11):2277-2288. [5]CHENG Z,CHAI S.A cyber intrusion detection method based on focal loss neural network[C]//2020 39th Chinese Control Conference(CCC).IEEE,2020. [6]ZHOU Y J,HE P F,QIU R F,et al.Research on Intrusion Detection Based on Random Forest and Gradient Boosting Tree[J].Journal of Software,2021,32(10):3254-3265. [7]LIU Y,YANG K.Credit Fraud Detection for Extremely Imba-lanced Data Based on Ensembled Deep Learning[J].Journal of Computer Research and Development,2021,58(3):539-547. [8]POURHABIBI T,ONG K L,KAM B H,et al.Fraud detection:A systematic literature review of graph-based anomaly detection approaches[J].Decision Support Systems,2020,133:113303. [9]AL-HASHEDI K G,MAGALINGAM P.Financial fraud detection applying data mining techniques:A comprehensive review from 2009 to 2019[J].Computer Science Review2021,40:100402. [10]FIORE U,AD S,PERLA F,et al.Using generative adversarial networks for improving classification effectiveness in credit card fraud detection[J].Information Sciences,2019,479:448-455. [11]FERNANDO T,GAMMULLE H,DENMAN S,et al.Deeplearning for medical anomaly detection-a survey[J].ACM Computing Surveys(CSUR),2021,54(7):1-37. [12]HAN C,RUNDO L,MURAO K,et al.MADGAN:Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction[J].BMC bioinformatics,2021,22(2):1-20. [13]SHVETSOVA N,BAKKER B,FEDULOVA I,et al.Anomaly detection in medical imaging with deep perceptual autoencoders[J].IEEE Access,2021,9:118571-118583. [14]POORNIMA I,PARAMASIVAN B.Anomaly detection in wireless sensor network using machine learning algorithm[J].Computer communications,2020,151:331-337. [15]FRANCESCO C,GIANCARLO F,ANTONIO G,et al.Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance[J].Information Fusion,2019,52:13-30. [16]ZHOU J T,DU J,ZHU H,et al.Anomalynet:An anomaly detection network for video surveillance[J].IEEE Transactions on Information Forensics and Security,2019,14(10):2537-2550. [17]SULTANI W,CHEN C,SHAH M.Real-world anomaly detection in surveillance videos[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018. [18]CHANDOLA V,BANERJEE A,KUMAR V.Anomaly detec-tion:A survey[J].ACM computing surveys(CSUR),2009,41(3):1-58. [19]XU X,LIU J W,LUO X L.Research on outlier mining[J].Application Research of Computers,2009,26(1):34-40. [20]XUE A R,YAO L,JU S G,et al.Survey of Outlier Mining[J].Computer Science,2008(11):13-18,27. [21]MEI L,ZHANG F L,GAO Q.Overview of outlier detectiontechnology[J].Application Research of Computers,2020,37(12):3521-3527. [22]WU J F,JIN W D,TANG P.Survey on Monitoring Techniques for Data Abnormalities[J].Computer Science,2017,44(S2):24-28. [23]LEI H L,TUERHONG G,WUSHOUER M,et al.Review of Novelty Detection[J].Computer Engineering and Applications,2021,57(5):47-55. [24]JOHNSON T,KWOK I,NG R T.Fast Computation of 2-Dimensional Depth Contours[C]//KDD.1998:224-228. [25]KNOX E M,NG R T.Algorithms for mining distancebased outliers in large datasets[C]//Proceedings of the International Conference on Very Large Data Bases.1998:392-403. [26]RAMASWAMY S,RASTOGI R,SHIM K.Efficient algorithms for mining outliers from large data sets[C]//Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data.2000. [27]ESTER M,KRIEGEL H P,SANDER J,et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C]//KDD.1996:226-231. [28]ERTÖZ L,STEINBACH M,KUMAR V.Finding topics in collections of documents:A shared nearest neighbor approach[J].Clustering and information retrieval.Springer,Boston,MA,2004:83-103. [29]GUHA S,RASTOGI R,SHIM K.ROCK:A robust clustering algorithm for categorical attributes[J].Information systems,2000,25(5):345-66. [30]MACQUEEN J.Some methods for classification and analysis of multivariate observations[C]//Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability.1967:281-297. [31]KOHONEN T.Self-organization and associative memory[M].Springer Science & Business Media.2012. [32]HE Z,XU X,DENG S.Discovering cluster-based local outliers[J].Pattern recognition letters,2003,24(9/10):1641-1650. [33]AMER M,GOLDSTEIN M.Nearest-neighbor and clusteringbased anomaly detection algorithms for rapidminer[C]//Proceedings of the 3rd RapidMiner Community Meeting and Conference(RCOMM 2012).2012:1-12. [34]MUHAMMAD M,DANIEL ANI U,ABDULLAHI A A,et al.Device-Type Profiling for Network Access Control Systems using Clustering-Based Multivariate Gaussian Outlier Score[C]//The 5th International Conference on Future Networks & Distributed Systems.2021. [35]ALHUSSEIN I,ALI A H.Application of DBSCAN to Anomaly Detection in Airport Terminals[C]//2020 3rd International Conference on Engineering Technology and its Applications(IICETA).IEEE,2020. [36]ANKERST M,BREUNIG M M,KRIEGEL H P,et al.OP-TICS:Ordering points to identify the clustering structure[J].ACM Sigmod Record,1999,28(2):49-60. [37]BREUNIG M M,KRIEGEL H P,NG R T,et al.LOF:identi-fying density-based local outliers[C]//Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data.2000. [38]XU X,LEI Y,ZHOU X.A lof-based method for abnormal segment detection in machinery condition monitoring[C]//2018 Prognostics and System Health Management Conference(PHM-Chongqing).IEEE,2018. [39]TANG J,CHEN Z,FU A W C,et al.Enhancing effectiveness of outlier detections for low density patterns[C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining.Springer,Berlin,Heidelberg,2002. [40]JIN W,TUNG A K H,HAN J,et al.Ranking outliers using symmetric neighborhood relationship[C]//Pacific-Asia Confe-rence on Knowledge Discovery and Data Mining.Springer,Berlin,Heidelberg,2006. [41]KRIEGEL H P,KRÖGER P,SCHUBERT E,et al.LoOP:local outlier probabilities[C]//Proceedings of the 18th ACM Confe-rence on Information and Knowledge Management.2009. [42]PAPADIMITRIOU S,KITAGAWA H,GIBBONS P B,et al.Loci:Fast outlier detection using the local correlation integral[C]//Proceedings 19th International Conference on Data Engineering(Cat.No.03CH37405).IEEE,2003. [43]TANG B,HE H.A localdensity-based approach for outlier detection[J].Neurocomputing,2017,241:171-180. [44]KIRAN B R,THOMAS D M,PARAKKAL R.An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos[J].Journal of Imaging,2018,4(2):36. [45]CHEN Z,YEO C K,LEE B S,et al.Autoencoder-based network anomaly detection[C]//2018 Wireless Telecommunications Symposium(WTS).IEEE,2018. [46]WU Y K,LI W,NI M Y,et al.Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder[J].Computer Science,2022,49(3):144-151. [47]VINCENT P,LAROCHELLE H,LAJOIE I,et al.Stacked denoising autoencoders:Learning useful representations in a deep network with a local denoising criterion[J].Journal of Machine Learning Research,2010,11(12):3371-3408. [48]DOERSCH C.Tutorial on variational autoencoders[J].arXiv:1606.05908,2016. [49]ZHANG C H,ZHOU X T,ZHANG Y A,et al.Application Research of Deep Auto Encoder in Data Anomaly Detection[J].Computer Engineering and Applications,2020,56(17):93-99. [50]DI MATTIA F,GALEONE P,DE SIMONI M,et al.A survey on gans for anomaly detection[J].arXiv:1906.11632,2019. [51]SCHLEGL T,SEEBÖCK P,WALDSTEIN S M,et al.Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[C]//International Conference on Information Processing in Medical Imaging.Cham:Springer,2017:145-157. [52]ZENATI H,FOO C S,LECOUAT B,et al.Efficient gan-based anomaly detection[J].arXiv:1802.06222,2018. [53]SCHLEGL T,SEEBÖCK P,WALDSTEIN S M,et al.f-AnoGAN:Fast unsupervised anomaly detection with generative adversarial networks[J].Medical Image Analysis,2019,54:30-44. [54]DONAHUE J,KRÄHENBÜHL P,DARRELL T.Adversarial feature learning[J].arXiv:1605.09782,2016. [55]AKCAY S,ATAPOUR-ABARGHOUEI A,BRECKON T P.Ganomaly:Semi-supervised anomaly detection via adversarial training[C]//Asian Conference on Computer Vision.Cham:Springer,2018. [56]ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein generative adversarial networks[C]//International Conference on Machine Learning.PMLR,2017. [57]ZHU J Y,PARK T,ISOLA P,et al.Unpaired image-to-imagetranslation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision.2017. [58]ZAREMBA W,SUTSKEVER I,VINYALS O.Recurrent neural network regularization[J].arXiv:1409.2329,2014. [59]LIU F T,TING K M,ZHOU Z H.Isolation forest[C]//2008 Eighth IEEE International Conference on Data Mining.IEEE,2008:413-422. [60]LIU F T,TING K M,ZHOU Z H.On detecting clustered anomalies using sciforest[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Sprin-ger,Berlin,Heidelberg,2010. [61]ZHONG Y Y,CHEN S C.High-order Multi-view Outlier Detection[J].Computer Science,2020,47(9):99-104. [62]AGGARWAL C C,YU P S.Outlier detection for high dimen-sional data[C]//Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data.2001. [63]KRIEGEL H P,SCHUBERT M,ZIMEK A.Angle-based outlier detection inhigh-dimensional data[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2008. [64]KRIEGEL H P,KRÖGER P,SCHUBERT E,et al.Outlier detection in axis-parallel subspaces of high dimensional data[C]//Pacific-asia Conference on Knowledge Discovery and Data Mi-ning.Springer,Berlin,Heidelberg,2009. [65]KELLER F,MULLER E,BOHM K.HiCS:High contrast subspaces for density-based outlierranking[C]//2012 IEEE 28th International Conference on Data Engineering.IEEE,2012. [66]CHEN S N,QIAN H Y,LI W.Hybrid outlier detection algo-rithm based on angle variance for high-dimensional data[J].Application Research of Computers,2016,33(11):3383-3386. [67]PHAM N.L1-depth revisited:A robust angle-based outlier factor in high-dimensional space[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Cham:Springer,2018. [68]CHANDOLA V,MITHAL V,KUMAR V.Comparative evaluation of anomaly detection techniques for sequence data[C]//2008 Eighth IEEE International Conference on Data Mining.IEEE,2008. [69]HAWKINS J,AHMAD S.Why neurons have thousands of sy-napses,a theory of sequence memory in neocortex[J].Frontiers in neural circuits,2016,10:174222. [70]AHMAD S,LAVIN A,PURDY S,et al.Unsupervised real-timeanomaly detection for streaming data[J].Neurocomputing,2017,262:134-147. [71]XU J,WU H,WANG J,et al.Anomaly Transformer:Time Series Anomaly Detection with Association Discrepancy[J].arXiv:2110.02642,2021. [72]DEAN J,GHEMAWAT S.MapReduce:Simplified data proces-sing on large clusters[J].Communications of ACM,2008,51(1):107-113. [73]ZAHARIA M,CHOWDHURY M,DAS T,et al.Resilient Distributed Datasets:A {Fault-Tolerant} Abstraction for {In-Memory} Cluster Computing[C]//9th USENIX Symposium on Networked Systems Design and Implementation(NSDI 12).2012. [74]KANNA P R,SANTHI P.Hybrid intrusion detection using mapreduce based black widow optimized convolutional long short-term memory neural networks[J].Expert Systems with Applications,2022,194:116545. [75]FATHNIA F,BARAZESH M R,BAYAZ M H J D.RuntimeOptimization of a New Anomaly Detection Method for Smart Metering Data Using Hadoop Map-Reduce[C]//2019 International Power System Conference(PSC).IEEE,2019. [76]ALNAFESSAH A,CASALE G.Artificial neural networksbased techniques for anomaly detection in Apache Spark[J].Cluster Computing,2020,23(2):1345-1360. [77]POURHABIBI T,ONG K L,KAM B H,et al.Fraud detection:A systematic literature review of graph-based anomaly detection approaches[J].Decision Support Systems,2020,133:113303. [78]MA X,WU J,XUE S,et al.A comprehensive survey on graphanomaly detection with deep learning[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(12):12012-12038. [79]CHEN B F,LI J D,LU X J,et al.Survey of Deep Learning Based Graph Anomaly Detection Methods[J].Journal of Computer Research and Development,2021,58(7):1436-1455. [80]MOONESINGHE H D K,TAN P N.Outrank:a graph-based outlier detection framework using random walk[J].Interna-tional Journal on Artificial Intelligence Tools,2008,17(1):19-36. [81]BANDYOPADHYAY S,VIVEK S V,MURTY M N.Outlierresistant unsupervised deep architectures for attributed network embedding[C]//Proceedings of the 13th International Confe-rence on Web Search and Data Mining.2020. [82]SU J,DONG Y H,YAN M J,et al.Research progress of anomaly detectionfor complex networks[J].Control and Decision,2021,36(6):1293-1310. [83]MOJARAD M,NEJATIAN S,PARVIN H,et al.A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters[J].Applied Intelligence,2019,49:2567-2581. [84]GUO Y L,ZUO X J,CUI J Y.An abnormal behavior detection algorithm based on fuzzy clusteringfor multi-categories affiliation of power entities[J].Journal of Hebei University of Science and Technology,2022,43(5):528-537. [85]CHEN Z,SHENG V,EDWARDS A,et al.An effective cost-sensitive sparse online learning framework for imbalanced streaming data classification and its application to online anomaly detection[J].Knowledge and Information Systems,2023,65(1):59-87. [86]CHEN X,LIU H,XU X,et al.Identification of Suitable Technologies for Drinking Water Quality Prediction:A Comparative Study of Traditional,Ensemble,Cost-Sensitive,Outlier Detection Learning Models and Sampling Algorithms[J].ACS ES&T Water,2021,1(8):1676-1685. [87]BISONG E.Introduction to Scikit-learn[C]//Building machine learning and deep learning models on Google cloud platform.Apress,Berkeley,CA,2019:215-229. [88]ZHAO Y,NASRULLAH Z,LI Z.Pyod:A python toolbox for scalable outlier detection[J].arXiv:1901.01588,2019. [89]SCHUBERT E,ZIMEK A.ELKI:A large open-source libraryfor data analysis-ELKI Release 0.7.5 “Heidelberg”[J].arXiv:1902.03616,2019. [90]FU L F,CHEN Z,AO C L.Dynamic outlier detection algorithm for network large data set based on classification and regression trees decision tree[J].Journal of Jilin University(Engineering and Technology Edition),2023,53(9):2620-2625. [91]HUANG J R,WANG Q,CAI X J,et al.Multi-objective Adaptive DBSCAN Outlier Detection Algorithm[J].Journal of Chinese Computer Systems,2022,43(4):702-706. |
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