More Web Proxy on the site http://driver.im/

research-article

Deep Learning for Anomaly Detection: A Review

Authors:

Anton Van Den HengelAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 2

Article No.: 38, Pages 1 - 38

https://doi.org/10.1145/3439950

Published: 05 March 2021 Publication History

Abstract

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

Supplementary Material

a38-pang-suppl.pdf (pang.zip)

Supplemental movie, appendix, image and software files for, Deep Learning for Anomaly Detection: A Review

Download
32.30 KB

References

[1]

Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara. 2019. Latent space autoregression for novelty detection. In CVPR. 481--490.

[2]

Charu C. Aggarwal. 2017. Outlier Analysis. Springer.

[3]

Samet Akcay, Amir Atapour-Abarghouei, and Toby P. Breckon. 2018. GANomaly: Semi-supervised anomaly detection via adversarial training. In ACCV. Springer, 622--637.

[4]

Leman Akoglu, Hanghang Tong, and Danai Koutra. 2015. Graph based anomaly detection and description: A survey. Data Min. Knowl. Discov. 29, 3 (2015), 626--688.

Digital Library

[5]

Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Maximilian Strobel, and Daniel Cremers. 2018. Clustering with deep learning: Taxonomy and new methods. arXiv:1801.07648. Retrieved from https://arxiv.org/abs/1801.07648.

[6]

J. Andrews, Thomas Tanay, Edward J. Morton, and Lewis D. Griffin. 2016. Transfer representation-learning for anomaly detection. In PMLR.

[7]

Fabrizio Angiulli, Fabio Fassetti, Giuseppe Manco, and Luigi Palopoli. 2017. Outlying property detection with numerical attributes. Data Min. Knowl. Discov. 31, 1 (2017), 134--163.

Digital Library

[8]

Fabrizio Angiulli, Fabio Fassetti, and Luigi Palopoli. 2009. Detecting outlying properties of exceptional objects. ACM Trans. Database Syst. 34, 1 (2009), 1--62.

Digital Library

[9]

Fabrizio Angiulli and Clara Pizzuti. 2002. Fast outlier detection in high dimensional spaces. In PKDD. Springer, 15--27.

[10]

Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein generative adversarial networks. In ICML. 214--223.

[11]

Terje Aven. 2016. Risk assessment and risk management: Review of recent advances on their foundation. Eur. J. Operat. Res. 253, 1 (2016), 1--13.

[12]

Fatemeh Azmandian, Ayse Yilmazer, Jennifer G. Dy, Javed A. Aslam, and David R. Kaeli. 2012. GPU-accelerated feature selection for outlier detection using the local kernel density ratio. In ICDM. IEEE, 51--60.

[13]

Kevin Bache and Moshe Lichman. 2013. UCI machine learning repository. Retrieved from http://archive.ics.uci.edu/ml.

[14]

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 8 (2013), 1798--1828.

Digital Library

[15]

Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2019. MVTec AD--A comprehensive real-world dataset for unsupervised anomaly detection. In CVPR. 9592--9600.

[16]

Azzedine Boukerche, Lining Zheng, and Omar Alfandi. 2020. Outlier detection: Methods, models and classifications. Comput. Surv. (2020).

[17]

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander. 2000. LOF: Identifying density-based local outliers. ACM SIGMOD Rec. 29, 2 (2000), 93--104.

Digital Library

[18]

Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A. Efros. 2019. Large-scale study of curiosity-driven learning. In ICLR.

[19]

Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov. 2019. Exploration by random network distillation. In ICLR.

[20]

Guilherme O. Campos, Arthur Zimek, Jörg Sander, Ricardo J. G. B. Campello, Barbora Micenková, Erich Schubert, Ira Assent, and Michael E. Houle. 2016. On the evaluation of unsupervised outlier detection: Measures, datasets, and an empirical study. Data Min. Knowl. Discov. 30, 4 (2016), 891--927.

Digital Library

[21]

Emmanuel J. Candès, Xiaodong Li, Yi Ma, and John Wright. 2011. Robust principal component analysis? J. ACM 58, 3 (2011), 1--37.

Digital Library

[22]

Longbing Cao. 2015. Coupling learning of complex interactions. Inf. Process. Manage. 51, 2 (2015), 167--186.

[23]

Longbing Cao, Philip S. Yu, Chengqi Zhang, and Yanchang Zhao. 2010. Domain Driven Data Mining. Springer.

[24]

Wei Cao and Longbing Cao. 2015. Financial crisis forecasting via coupled market state analysis. IEEE Intell. Syst. 30, 2 (2015), 18--25.

Digital Library

[25]

Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. 2018. Deep clustering for unsupervised learning of visual features. In ECCV. 132--149.

[26]

Raghavendra Chalapathy and Sanjay Chawla. 2019. Deep learning for anomaly detection: A survey. arXiv:1901.03407. Retrieved from https://arxiv.org/abs/1901.03407.

[27]

Raghavendra Chalapathy, Aditya Krishna Menon, and Sanjay Chawla. 2018. Anomaly detection using one-class neural networks. arXiv:1802.06360. Retrieved from https://arxiv.org/abs/1802.06360.

[28]

Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. Comput. Surv. 41, 3 (2009), 15.

Digital Library

[29]

Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga. 2017. Outlier detection with autoencoder ensembles. In SDM. 90--98.

[30]

Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, and Kai Zhang. 2016. Entity embedding-based anomaly detection for heterogeneous categorical events. In IJCAI. 1396--1403.

[31]

Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Mach. Learn. 20, 3 (1995), 273--297.

[32]

Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A. Bharath. 2018. Generative adversarial networks: An overview. IEEE Sign. Process. Mag. 35, 1 (2018), 53--65.

[33]

Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, and Russ R. Salakhutdinov. 2017. Good semi-supervised learning that requires a bad gan. In NeurIPS. 6510--6520.

[34]

Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, Cesare Alippi, and Gianluca Bontempi. 2017. Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Trans. Neural Netw. Learn. Syst. 29, 8 (2017), 3784--3797.

[35]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. Retrieved from https://arxiv.org/abs/1810.04805.

[36]

Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C. H. Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan. 2017. Deep unsupervised clustering with gaussian mixture variational autoencoders. In ICLR.

[37]

Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. 2019. Deep anomaly detection on attributed networks. In SDM. 594--602.

[38]

Carl Doersch. 2016. Tutorial on variational autoencoders. arXiv:1606.05908. Retrieved from https://arxiv.org/abs/1606.05908.

[39]

Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell. 2017. Adversarial feature learning. In ICLR.

[40]

Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar. 2017. Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In CCS. 1285--1298.

Digital Library

[41]

Mengnan Du, Ninghao Liu, and Xia Hu. 2019. Techniques for interpretable machine learning. Commun. ACM 63, 1 (2019), 68--77.

Digital Library

[42]

Lei Duan, Guanting Tang, Jian Pei, James Bailey, Akiko Campbell, and Changjie Tang. 2015. Mining outlying aspects on numeric data. Data Min. Knowl. Discov. 29, 5 (2015), 1116--1151.

Digital Library

[43]

Andrew F. Emmott, Shubhomoy Das, Thomas Dietterich, Alan Fern, and Weng-Keen Wong. 2013. Systematic construction of anomaly detection benchmarks from real data. In KDD Workshop. 16--21.

Digital Library

[44]

Sarah M. Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, and Christopher Leckie. 2016. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recogn. 58 (2016), 121--134.

Digital Library

[45]

Shaohua Fan, Chuan Shi, and Xiao Wang. 2018. Abnormal event detection via heterogeneous information network embedding. In CIKM. 1483--1486.

[46]

Soroush Fatemifar, Shervin Rahimzadeh Arashloo, Muhammad Awais, and Josef Kittler. 2019. Spoofing attack detection by anomaly detection. In ICASSP. IEEE, 8464--8468.

[47]

Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng, Weidong Cai, and Heng Huang. 2017. Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. In ICCV. 5736--5745.

[48]

Izhak Golan and Ran El-Yaniv. 2018. Deep anomaly detection using geometric transformations. In NeurIPS. 9758--9769.

[49]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press.

Digital Library

[50]

Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, and Daan Wierstra. 2014. Deep AutoRegressive networks. In ICML. 1242--1250.

[51]

Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel. 2017. On the (statistical) detection of adversarial examples. arXiv:1702.06280. Retrieved from https://arxiv.org/abs/1702.06280.

[52]

Frank E Grubbs. 1969. Procedures for detecting outlying observations in samples. Technometrics 11, 1 (1969), 1--21.

[53]

Manish Gupta, Jing Gao, Charu C. Aggarwal, and Jiawei Han. 2013. Outlier detection for temporal data: A survey. IEEE Trans. Knowl. Data Eng. 26, 9 (2013), 2250--2267.

[54]

Michael Gutmann and Aapo Hyvärinen. 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS. 297--304.

[55]

R. Hadsell, S. Chopra, and Y. LeCun. 2006. Dimensionality reduction by learning an invariant mapping. In CVPR, Vol. 2. 1735--1742.

[56]

Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, and Larry S. Davis. 2016. Learning temporal regularity in video sequences. In CVPR. 733--742.

[57]

Simon Hawkins, Hongxing He, Graham Williams, and Rohan Baxter. 2002. Outlier detection using replicator neural networks. In DaWaK.

[58]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.

[59]

Zengyou He, Xiaofei Xu, and Shengchun Deng. 2003. Discovering cluster-based local outliers. Pattern Recogn. Lett. 24, 9--10 (2003), 1641--1650.

Digital Library

[60]

Dan Hendrycks and Kevin Gimpel. 2017. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In ICLR.

[61]

Geoffrey E. Hinton and Ruslan R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313, 5786 (2006), 504--507.

[62]

Victoria Hodge and Jim Austin. 2004. A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 2 (2004), 85--126.

Digital Library

[63]

Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F Fei-Fei, and Juan Carlos Niebles. 2018. Learning to decompose and disentangle representations for video prediction. In NeurIPS. 517--526.

[64]

Yi-an Huang, Wei Fan, Wenke Lee, and Philip S. Yu. 2003. Cross-feature analysis for detecting ad-hoc routing anomalies. In ICDCS. IEEE, 478--487.

[65]

Radu Tudor Ionescu, Fahad Shahbaz Khan, Mariana-Iuliana Georgescu, and Ling Shao. 2019. Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. In CVPR. 7842--7851.

[66]

Radu Tudor Ionescu, Sorina Smeureanu, Bogdan Alexe, and Marius Popescu. 2017. Unmasking the abnormal events in video. In ICCV. 2895--2903.

[67]

Mon-Fong Jiang, Shian-Shyong Tseng, and Chih-Ming Su. 2001. Two-phase clustering process for outliers detection. Pattern Recogn. Lett. 22, 6--7 (2001), 691--700.

Digital Library

[68]

ShengYi Jiang, Xiaoyu Song, Hui Wang, Jian-Jun Han, and Qing-Hua Li. 2006. A clustering-based method for unsupervised intrusion detections. Pattern Recogn. Lett. 27, 7 (2006), 802--810.

Digital Library

[69]

Xinwei Jiang, Junbin Gao, Xia Hong, and Zhihua Cai. 2014. Gaussian processes autoencoder for dimensionality reduction. In PAKDD. Springer, 62--73.

[70]

Fabian Keller, Emmanuel Muller, and Klemens Bohm. 2012. HiCS: High contrast subspaces for density-based outlier ranking. In ICDE. IEEE, 1037--1048.

[71]

Tung Kieu, Bin Yang, Chenjuan Guo, and Christian S. Jensen. 2019. Outlier detection for time series with recurrent autoencoder ensembles. In IJCAI.

[72]

Edwin M. Knorr and Raymond T. Ng. 1999. Finding intensional knowledge of distance-based outliers. In VLDB, Vol. 99. 211--222.

[73]

Edwin M. Knorr, Raymond T. Ng, and Vladimir Tucakov. 2000. Distance-based outliers: Algorithms and applications. VLDB J. 8, 3--4 (2000), 237--253.

Digital Library

[74]

Hans-Peter Kriegel, Peer Kroger, Erich Schubert, and Arthur Zimek. 2011. Interpreting and unifying outlier scores. In SDM. 13--24.

[75]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In NeurIPS. 1097--1105.

Digital Library

[76]

Srijan Kumar, Francesca Spezzano, and V. S. Subrahmanian. 2015. Vews: A wikipedia vandal early warning system. In KDD. 607--616.

Digital Library

[77]

Aleksandar Lazarevic and Vipin Kumar. 2005. Feature bagging for outlier detection. In KDD. ACM, 157--166.

[78]

Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278--2324.

[79]

Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin. 2018. Training confidence-calibrated classifiers for detecting out-of-distribution samples. In ICLR.

[80]

Ping Li, Trevor J. Hastie, and Kenneth W. Church. 2006. Very sparse random projections. In KDD. 287--296.

[81]

Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos. 2013. Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1 (2013), 18--32.

[82]

Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, and Fei Wu. 2018. Deep sequence learning with auxiliary information for traffic prediction. In KDD. 537--546.

[83]

Weixian Liao, Yifan Guo, Xuhui Chen, and Pan Li. 2018. A unified unsupervised gaussian mixture variational autoencoder for high dimensional outlier detection. In IEEE Big Data. IEEE, 1208--1217.

[84]

Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2012. Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6, 1 (2012), 3.

Digital Library

[85]

Tie-Yan Liu et al. 2009. Learning to rank for information retrieval. Found. Trends Inf. Retriev. 3, 3 (2009), 225--331.

[86]

Wen Liu, Weixin Luo, Dongze Lian, and Shenghua Gao. 2018. Future frame prediction for anomaly detection--A new baseline. In CVPR. 6536--6545.

[87]

Xialei Liu, Joost van de Weijer, and Andrew D. Bagdanov. 2018. Leveraging unlabeled data for crowd counting by learning to rank. In CVPR. 7661--7669.

[88]

Yusha Liu, Chun-Liang Li, and Barnabás Póczos. 2018. Classifier two sample test for video anomaly detection. In BMVC.

[89]

Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, and Xiangnan He. 2019. Generative adversarial active learning for unsupervised outlier detection. IEEE Trans. Knowl. Data Eng. (2019).

[90]

Cewu Lu, Jianping Shi, and Jiaya Jia. 2013. Abnormal event detection at 150 fps in matlab. In ICCV. 2720--2727.

[91]

Weining Lu, Yu Cheng, Cao Xiao, Shiyu Chang, Shuai Huang, Bin Liang, and Thomas Huang. 2017. Unsupervised sequential outlier detection with deep architectures. IEEE Trans. Image Process. 26, 9 (2017), 4321--4330.

Digital Library

[92]

Weixin Luo, Wen Liu, and Shenghua Gao. 2017. Remembering history with convolutional lstm for anomaly detection. In ICME. IEEE, 439--444.

[93]

Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker. 2009. Identifying suspicious URLs: An application of large-scale online learning. In ICML. ACM, 681--688.

[94]

Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos. 2010. Anomaly detection in crowded scenes. In CVPR. 1975--1981.

[95]

Alireza Makhzani and Brendan Frey. 2014. K-sparse autoencoders. In ICLR.

[96]

Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2016. LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv:1607.00148. Retrieved from https://arxiv.org/abs/1607.00148.

[97]

Erik Marchi, Fabio Vesperini, Felix Weninger, Florian Eyben, Stefano Squartini, and Björn Schuller. 2015. Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection. In IJCNN. IEEE, 1--7.

[98]

Michael Mathieu, Camille Couprie, and Yann LeCun. 2016. Deep multi-scale video prediction beyond mean square error. In ICLR.

[99]

Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein. 2017. Unrolled generative adversarial networks. In ICLR.

[100]

Nour Moustafa and Jill Slay. 2015. UNSW-NB15: A comprehensive data set for network intrusion detection systems. In MilCIS. 1--6.

[101]

Mary M. Moya, Mark W. Koch, and Larry D. Hostetler. 1993. One-class Classifier Networks for Target Recognition Applications. Technical Report. NASA STI/Recon Technical Report N.

[102]

Andrew Y. Ng and Stuart J. Russell. 2000. Algorithms for inverse reinforcement learning. In ICML. Morgan Kaufmann Publishers Inc., 663--670.

[103]

Cuong Phuc Ngo, Amadeus Aristo Winarto, Connie Kou Khor Li, Sojeong Park, Farhan Akram, and Hwee Kuan Lee. 2019. Fence GAN: Towards better anomaly detection. arXiv:1904.01209. Retrieved from https://arxiv.org/abs/1904.01209.

[104]

Minh-Nghia Nguyen and Ngo Anh Vien. 2018. Scalable and interpretable one-class svms with deep learning and random fourier features. In ECML-PKDD. Springer, 157--172.

[105]

Keith Noto, Carla Brodley, and Donna Slonim. 2012. FRaC: A feature-modeling approach for semi-supervised and unsupervised anomaly detection. Data Min. Knowl. Discov. 25, 1 (2012), 109--133.

Digital Library

[106]

University of Minnesota. 2020. UMN Unusual Crowd Activity data set. Retrieved May 30, 2020 from http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi.

[107]

Min-hwan Oh and Garud Iyengar. 2019. Sequential anomaly detection using inverse reinforcement learning. In KDD. 1480--1490.

[108]

Guansong Pang. 2019. Non-IID Outlier Detection with Coupled Outlier Factors. Ph.D. Dissertation.

[109]

Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, and Huan Liu. 2018. Sparse modeling-based sequential ensemble learning for effective outlier detection in high-dimensional numeric data. In AAAI. 3892--3899.

[110]

Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu. 2016. Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings. In ICDM. IEEE, 410--419.

[111]

Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu. 2017. Learning homophily couplings from non-IID data for joint feature selection and noise-resilient outlier detection. In IJCAI. 2585--2591.

[112]

Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu. 2018. Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. In KDD. 2041--2050.

[113]

Guansong Pang, Anton van den Hengel, Chunhua Shen, and Longbing Cao. 2020. Deep reinforcement learning for unknown anomaly detection. arXiv:2009.06847. Retrieved from https://arxiv.org/abs/2009.06847.

[114]

Guansong Pang, Chunhua Shen, Huidong Jin, and Anton van den Hengel. 2019. Deep weakly-supervised anomaly detection. arXiv:1910.13601. Retrieved from https://arxiv.org/abs/1910.13601.

[115]

Guansong Pang, Chunhua Shen, and Anton van den Hengel. 2019. Deep anomaly detection with deviation networks. In KDD. 353--362.

[116]

Guansong Pang, Kai Ming Ting, and David Albrecht. 2015. LeSiNN: Detecting anomalies by identifying least similar nearest neighbours. In ICDM Workshop. IEEE, 623--630.

Digital Library

[117]

Guansong Pang, Cheng Yan, Chunhua Shen, Anton van den Hengel, and Xiao Bai. 2020. Self-trained deep ordinal regression for end-to-end video anomaly detection. In CVPR. 12173--12182.

[118]

Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell. 2017. Curiosity-driven exploration by self-supervised prediction. In ICML. 2778--2787.

[119]

Andrea Paudice, Luis Muñoz-González, Andras Gyorgy, and Emil C. Lupu. 2018. Detection of adversarial training examples in poisoning attacks through anomaly detection. arXiv:1802.03041. Retrieved from https://arxiv.org/abs/1802.03041.

[120]

Pramuditha Perera, Ramesh Nallapati, and Bing Xiang. 2019. OCGAN: One-class novelty detection using gans with constrained latent representations. In CVPR. 2898--2906.

[121]

Daniel Pérez-Cabo, David Jiménez-Cabello, Artur Costa-Pazo, and Roberto J. López-Sastre. 2019. Deep anomaly detection for generalized face anti-spoofing. In CVPR Workshops.

[122]

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In NAACL-HLT. 2227--2237.

[123]

Tomáš Pevnỳ. 2016. Loda: Lightweight on-line detector of anomalies. Mach. Learn. 102, 2 (2016), 275--304.

Digital Library

[124]

Ali Rahimi and Benjamin Recht. 2008. Random features for large-scale kernel machines. In NeurIPS. 1177--1184.

[125]

Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim. 2000. Efficient algorithms for mining outliers from large data sets. In SIGMOD. 427--438.

[126]

Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim. 2000. Efficient algorithms for mining outliers from large data sets. ACM SIGMOD Rec. 29, 2 (2000), 427--438.

Digital Library

[127]

Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan. 2019. Likelihood ratios for out-of-distribution detection. In NeurIPS. 14680--14691.

[128]

Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio. 2011. Contractive auto-encoders: Explicit invariance during feature extraction. In ICML. 833--840.

[129]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In MICCAI. Springer, 234--241.

[130]

Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto, Michele Piana, and Alessandro Verri. 2004. Are loss functions all the same? Neural Comput. 16, 5 (2004), 1063--1076.

Digital Library

[131]

Volker Roth. 2005. Outlier detection with one-class kernel fisher discriminants. In NeurIPS. 1169--1176.

[132]

Lukas Ruff, Nico Görnitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert Vandermeulen, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018. Deep one-class classification. In ICML. 4390--4399.

[133]

Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft. 2020. Deep semi-supervised anomaly detection. In ICLR.

[134]

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. 2015. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 3 (2015), 211--252.

Digital Library

[135]

Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli. 2018. Adversarially learned one-class classifier for novelty detection. In CVPR. 3379--3388.

[136]

Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training gans. In NeurIPS. 2234--2242.

[137]

Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, and Ursula Schmidt-Erfurth. 2019. f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54 (2019), 30--44.

[138]

Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. 2017. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In IPMI. Springer, Cham, 146--157.

[139]

Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, and Robert C. Williamson. 2001. Estimating the support of a high-dimensional distribution. Neural Comput. 13, 7 (2001), 1443--1471.

Digital Library

[140]

Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller. 1997. Kernel principal component analysis. In ICANN. 583--588.

[141]

Erich Schubert, Jörg Sander, Martin Ester, Hans Peter Kriegel, and Xiaowei Xu. 2017. DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN. ACM Trans. Database Syst. 42, 3 (2017), 1--21.

Digital Library

[142]

Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, and Weng-Keen Wong. 2019. Sequential feature explanations for anomaly detection. ACM Trans. Knowl. Discov. Data 13, 1 (2019), 1--22.

Digital Library

[143]

Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In ICLR.

[144]

Mahito Sugiyama and Karsten Borgwardt. 2013. Rapid distance-based outlier detection via sampling. In NeurIPS. 467--475.

[145]

Waqas Sultani, Chen Chen, and Mubarak Shah. 2018. Real-world anomaly detection in surveillance videos. In CVPR. 6479--6488.

[146]

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In NeurIPS. 3104--3112.

Digital Library

[147]

Acar Tamersoy, Kevin Roundy, and Duen Horng Chau. 2014. Guilt by association: Large scale malware detection by mining file-relation graphs. In KDD. 1524--1533.

[148]

David M. J. Tax and Robert P. W. Duin. 2004. Support vector data description. Mach. Learn. 54, 1 (2004), 45--66.

Digital Library

[149]

Lena Tenenboim-Chekina, Lior Rokach, and Bracha Shapira. 2013. Ensemble of feature chains for anomaly detection. In MCS. 295--306.

[150]

Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszár. 2017. Lossy image compression with compressive autoencoders. In ICLR.

[151]

Fei Tian, Bin Gao, Qing Cui, Enhong Chen, and Tie-Yan Liu. 2014. Learning deep representations for graph clustering. In AAAI. 1293--1299.

[152]

Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W. Verjans, and Gustavo Carneiro. 2020. Few-shot anomaly detection for polyp frames from colonoscopy. In MICCAI.

[153]

Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11 (Dec. 2010), 3371--3408.

Digital Library

[154]

Nguyen Xuan Vinh, Jeffrey Chan, Simone Romano, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, and Jian Pei. 2016. Discovering outlying aspects in large datasets. Data Mining and Knowledge Discovery 30, 6 (2016), 1520--1555.

Digital Library

[155]

Hu Wang, Guansong Pang, Chunhua Shen, and Congbo Ma. 2020. Unsupervised representation learning by predicting random distances. In IJCAI.

[156]

Hao Wang and Dit-Yan Yeung. 2016. Towards Bayesian deep learning: A framework and some existing methods. IEEE Trans. Knowl. Data Eng. 28, 12 (2016), 3395--3408.

Digital Library

[157]

Siqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, and Marius Kloft. 2019. Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network. In NeurIPS. 5960--5973.

[158]

Xuanhui Wang, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork. 2018. Position bias estimation for unbiased learning to rank in personal search. In WSDM. 610--618.

[159]

Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. 2020. Generalizing from a few examples: A survey on few-shot learning. Comput. Surv. 53, 3 (2020), 1--34.

Digital Library

[160]

Steve Webb, James Caverlee, and Calton Pu. 2006. Introducing the webb spam corpus: Using email spam to identify web spam automatically. In CEAS.

[161]

Peng Wu, Jing Liu, and Fang Shen. 2019. A deep one-class neural network for anomalous event detection in complex scenes. IEEE Trans. Neural Netw. Learn. Syst. (2019).

[162]

Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. In ICML. 478--487.

[163]

Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, and Nicu Sebe. 2015. Learning deep representations of appearance and motion for anomalous event detection. In BMVC.

[164]

Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael Jordan. 2009. Online system problem detection by mining patterns of console logs. In ICDM. IEEE, 588--597.

[165]

Jianwei Yang, Devi Parikh, and Dhruv Batra. 2016. Joint unsupervised learning of deep representations and image clusters. In CVPR. 5147--5156.

[166]

Xu Yang, Cheng Deng, Feng Zheng, Junchi Yan, and Wei Liu. 2019. Deep spectral clustering using dual autoencoder network. In CVPR. 4066--4075.

[167]

Muchao Ye, Xiaojiang Peng, Weihao Gan, Wei Wu, and Yu Qiao. 2019. Anopcn: Video anomaly detection via deep predictive coding network. In ACM MM. 1805--1813.

Digital Library

[168]

Wenchao Yu, Wei Cheng, Charu C. Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang. 2018. Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks. In KDD. 2672--2681.

Digital Library

[169]

Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, and Seung-Ik Lee. 2020. Old is gold: Redefining the adversarially learned one-class classifier training paradigm. In CVPR. 14183--14193.

[170]

Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. 2018. Efficient gan-based anomaly detection. arXiv:1802.06222. Retrieved from https://arxiv.org/abs/1802.06222.

[171]

Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar. 2018. Adversarially learned anomaly detection. In ICDM. IEEE, 727--736.

[172]

Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, and Nitesh V. Chawla. 2019. A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In AAAI, Vol. 33. 1409--1416.

Digital Library

[173]

Ke Zhang, Marcus Hutter, and Huidong Jin. 2009. A new local distance-based outlier detection approach for scattered real-world data. In PAKDD. Springer, 813--822.

[174]

Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, and Aidong Lu. 2019. One-class adversarial nets for fraud detection. In AAAI. 1286--1293.

[175]

Chong Zhou and Randy C. Paffenroth. 2017. Anomaly detection with robust deep autoencoders. In KDD. ACM, 665--674.

[176]

Joey Tianyi Zhou, Jiawei Du, Hongyuan Zhu, Xi Peng, Yong Liu, and Rick Siow Mong Goh. 2019. Anomalynet: An anomaly detection network for video surveillance. IEEE Trans. Inf. Forens. Secur. 14, 10 (2019), 2537--2550.

[177]

Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In ICCV. 2223--2232.

[178]

Arthur Zimek, Erich Schubert, and Hans-Peter Kriegel. 2012. A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. 5, 5 (2012), 363--387.

Digital Library

[179]

Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In ICLR.

[180]

Hui Zou, Trevor Hastie, and Robert Tibshirani. 2006. Sparse principal component analysis. J. Comput. Graph. Stat. 15, 2 (2006), 265--286.

Cited By

Wang FJiang YZhang RWei AXie JPang X(2025)A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and DirectionsSensors10.3390/s2501019025:1(190)Online publication date: 1-Jan-2025
https://doi.org/10.3390/s25010190
Zhou YHuang ZZeng DQu YWu Z(2025)Dual-Branch Knowledge Distillation via Residual Features Aggregation Module for Anomaly SegmentationIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.350703674(1-11)Online publication date: 2025
https://doi.org/10.1109/TIM.2024.3507036
Faber KSniezynski BJapkowicz NCorizzo R(2025)pyCLAD: The universal framework for continual lifelong anomaly detectionSoftwareX10.1016/j.softx.2024.10199429(101994)Online publication date: Feb-2025
https://doi.org/10.1016/j.softx.2024.101994
Show More Cited By

Index Terms

Deep Learning for Anomaly Detection: A Review
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene anomaly detection
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Anomaly detection
    2. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation

Recommendations

Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities
WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

In this tutorial we aim to present a comprehensive survey of the advances in deep learning techniques specifically designed for anomaly detection (deep anomaly detection for short). Deep learning has gained tremendous success in transforming many data ...
Deep Anomaly Detection with Deviation Networks
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature ...
Deep Learning for Time Series Anomaly Detection: A Survey
Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence of anomalies can indicate novel or unexpected events, such ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 2

March 2022

800 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3450359

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 March 2021

Accepted: 01 November 2020

Revised: 01 October 2020

Received: 01 July 2020

Published in CSUR Volume 54, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1,361
Total Citations
View Citations
23,181
Total Downloads

Downloads (Last 12 months)5,415
Downloads (Last 6 weeks)666

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang FJiang YZhang RWei AXie JPang X(2025)A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and DirectionsSensors10.3390/s2501019025:1(190)Online publication date: 1-Jan-2025
https://doi.org/10.3390/s25010190
Zhou YHuang ZZeng DQu YWu Z(2025)Dual-Branch Knowledge Distillation via Residual Features Aggregation Module for Anomaly SegmentationIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.350703674(1-11)Online publication date: 2025
https://doi.org/10.1109/TIM.2024.3507036
Faber KSniezynski BJapkowicz NCorizzo R(2025)pyCLAD: The universal framework for continual lifelong anomaly detectionSoftwareX10.1016/j.softx.2024.10199429(101994)Online publication date: Feb-2025
https://doi.org/10.1016/j.softx.2024.101994
Lian XZheng YDang ZPeng CGao X(2025)Semi-supervised anomaly traffic detection via multi-frequency reconstructionPattern Recognition10.1016/j.patcog.2024.111215161(111215)Online publication date: May-2025
https://doi.org/10.1016/j.patcog.2024.111215
Cheng SSu XChen BChen HPeng DYuan Z(2025)GBMOD: A granular-ball mean-shift outlier detectorPattern Recognition10.1016/j.patcog.2024.111115159(111115)Online publication date: Mar-2025
https://doi.org/10.1016/j.patcog.2024.111115
Zeng XZhuo YLiao TGuo J(2025)Cloud-GANPattern Recognition10.1016/j.patcog.2024.110866157:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.patcog.2024.110866
Gao RWang JYu YWu JZhang L(2025)Enhanced graph diffusion learning with dynamic transformer for anomaly detection in multivariate time seriesNeurocomputing10.1016/j.neucom.2024.129168619(129168)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129168
Xi PCheng DLu GDeng ZZhang GZhang S(2025)Identifying local useful information for attribute graph anomaly detectionNeurocomputing10.1016/j.neucom.2024.128900617(128900)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128900
Zahid YZarges CTiddeman BHan J(2025)Adversarial diffusion for few-shot scene adaptive video anomaly detectionNeurocomputing10.1016/j.neucom.2024.128796614(128796)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128796
Parlak İEmel E(2025)Deep learning-based detection of internal defect types and their grades in high-pressure aluminum castingsMeasurement10.1016/j.measurement.2024.116119242(116119)Online publication date: Jan-2025
https://doi.org/10.1016/j.measurement.2024.116119
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents