[1]J. Hu, E. Zhu, S. Wang, X. Liu, X. Guo, and J. Yin, “An efficient and robust unsupervised anomaly detection method using ensemble random projection in surveillance videos,” Sensors, vol.19 no.19, p.4145, 2019.
[2]A. Lukashin, M. Popov, A. Bolshakov, and Y. Nikolashin, “Scalable data processing approach and anomaly detection method for user and entity behavior analytics platform,” In Intelligent Distributed Computing XIII, pp. 344-349, Springer International Publishing, 2020.
[3]C. Schwab, “User and Entity Behavior Analytics for Enterprise Security,” 2016.
[4]X. Xi, T. Zhang, W. Ye, Z. Wen, S. Zhang, D. Du, and Q. Gao, “An ensemble approach for detecting anomalous user behaviors,” Int. J. Softw. Eng. Knowl. Eng, vol.28(11n12), pp.1637-1656, 2018.
[5]S. Wang, Y. Zeng, Q. Liu, C. Zhu, E. Zhu, and J. Yin, “Detecting abnormality without knowing normality: A two-stage approach for unsupervised video abnormal event detection,” In Proceedings of the 26th ACM international conference on Multimedia, pp. 636-644, October 2018.
[6]J. Komulainen, A. Hadid, M. Pietikäinen, A. Anjos, and S. Marcel, “Complementary countermeasures for detecting scenic face spoofing attacks,” In Proceedings of 2013 International conference on biometrics (ICB), pp. 1-7, IEEE, June 2013,
[7]S.R. Arashloo, J. Kittler, and W. Christmas, “An anomaly detection approach to face spoofing detection: A new formulation and evaluation protocol,” IEEE Access, vol.5, pp.13868-13882, 2017.
[8]T. Chen, and C. Guestrin, “Xgboost: A scalable tree boosting system,” In Proceedings of the 22nd acmsigkdd international conference on knowledge discovery and data mining, pp. 785-794, August 2016.
[9]D. Li, D. Chen, B. Jin, L. Shi, J. Goh, and S.K. Ng, “MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks,” In Artificial Neural Networks and Machine Learning–ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV (pp. 703-716). Cham: Springer International Publishing, September 2019.
[10]D. Kwon, H. Kim, J. S.C., Kim, Suh, I. Kim, and K.J. Kim, “A survey of deep learning-based network anomaly detection,” Clust. Comput, vol. 22, pp.949-961, 2019.
[11]D.C. Le, and N. Zincir-Heywood, “Anomaly detection for insider threats using unsupervised ensembles,” IEEE Transactions on Network and Service Management, vol.18 no.2, pp.1152-1164, 2021.
[12]T. Wang, M. Qiao, Y. Deng, Y. Zhou, H. Wang, Q. Lyu, and H. Snoussi, “Abnormal event detection based on analysis of movement information of video sequence,” Optik, vol.152, pp.50-60, 2018.
[13]A.R.M. Forkan, I. Khalil, Z. Tari, S. Foufou, and A. Bouras, “A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living,” Pattern Recognit, vol.48 no.3, pp.628-641, 2015.
[14]M. Yamauchi, Y. Ohsita, M. Murata, K. Ueda, and Y. Kato, “Anomaly detection in smart home operation from user behaviors and home conditions,” IEEE Transactions on Consumer Electronics, vol.66 no.2, pp.183-192, 2020.
[15]P. Kamranfar, D. Lattanzi, A. Shehu, and D. Barbará, “Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets,” arXiv preprint arXiv:2210.01707, 2022.
[16]A.G. Martín, I.M. de Diego, A. Fernández-Isabel, M. Beltrán, and R.R. Fernández, “Combining user behavioural information at the feature level to enhance continuous authentication systems,” Knowledge-Based Syst, vol.244, pp.108544, 2022.
[17]M.S. Parwez, D.B. Rawat, and M. Garuba, “Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network,” IEEE Transactions on Industrial Informatics, vol.13 no.4, pp.2058-2065, 2017.
[18]F. Skopik, M. Wurzenberger, G. Höld, M. Landauer, and W. Kuhn, “Behavior-Based Anomaly Detection in Log Data of Physical Access Control Systems,” IEEE Transactions on Dependable and Secure Computing, 2022.
[19]Z. Liu, T. Qin, X. Guan, H. Jiang, and C. Wang, “An integrated method for anomaly detection from massive system logs,” IEEE Access, vol.6, pp.30602-30611, 2018
[20]D.J. Hill, and B.S. Minsker, “Anomaly detection in streaming environmental sensor data: A data-driven modeling approach,” Environ. Model. Softw, vol.25 no.9, pp.1014-1022, 2010.
[21]I.I.A. Sulayman, and A. Ouda, “User modeling via anomaly detection techniques for user authentication,” In 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0169-0176, IEEE, October 2019.
[22]B. Böse, B. Avasarala, S. Tirthapura, Y.Y. Chung, and D. Steiner, “Detecting insider threats using radish: A system for real-time anomaly detection in heterogeneous data streams,” IEEE Systems Journal, vol.11 no.2, pp.471-482, 2017.
[23]J. Cui, G. Zhang, Z. Chen, and N. Yu, “Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics,” Sci. Rep, vol.12 no.1, p.22349, 2022.
[24]G. Fernandes Jr, L.F. Carvalho, J.J.  Rodrigues, and M.L. Proença Jr, “Network anomaly detection using IP flows with principal component analysis and ant colony optimization,” J Netw Comput Appl, vol.64, pp.1-11, 2016.
[25]J. Hu, E. Zhu, S. Wang, S. Wang, X. Liu, and J. Yin, “Two-stage unsupervised video anomaly detection using low-rank based unsupervised one-class learning with ridge regression,” In Proceedings of 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, IEEE, July 2019.
[26]A.G. Martín, M. Beltrán, A. Fernández-Isabel, and I.M. de Diego, “An approach to detect user behaviour anomalies within identity federations,” Comput. Secur, vol.108, pp.102356, 2021.
[27]K.W. Cheng, Y.T. Chen, and W.H. Fang, “An efficient subsequence search for video anomaly detection and localization,” Multimed. Tools Appl, vol.75, pp.15101-15122, 2016.
[28]L. Feremans, V. Vercruyssen, B. Cule, W. Meert, and B. Goethals, “Pattern-based anomaly detection in mixed-type time series,” In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I (pp. 240-256). Springer International Publishing, 2020.
[29]M. Tahir, M. Li, X. Zheng, A. Carie, J. Xing, M. Azhar, N. Ayoub, A. Wagan, M. Aamir, L.A. Jamali, and M.A. Imran, “A novel network user behaviors and profile testing based on anomaly detection techniques,” Int J Adv Comput Sc, vol.10, no.6, 2019. 
[30]S. Lee, H.G. Kim, and Y.M. Ro, “Bman: Bidirectional multi-scale aggregation networks for abnormal event detection,” IEEE Transactions on Image Processing, vol.29, pp.2395-2408, 2019.
[31]W. Chen, Y. Zhang, C.K. Yeo, C.T. Lau, and B.S. Lee, “Unsupervised rumor detection based on users’ behaviors using neural networks,” Pattern Recognit. Lett, vol. 105, pp.226-233, 2018.
[32]T. Li, X. Chen, F. Zhu, Z. Zhang, and H. Yan, “Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection,” Neurocomputing, vol. 439, pp.256-270, 2021.
[33]S. Lin, R. Clark, R. Birke, S. Schönborn, N.  Trigoni, and S. Roberts, “Anomaly detection for time series using vae-lstm hybrid model,” In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4322-4326, May, 2020.
[34]S. Yan, J.S. Smith, W. Lu, and B. Zhang, “Abnormal event detection from videos using a two-stream recurrent variational autoencoder,” IEEE Transactions on Cognitive and Developmental Systems, vol.12 no.1, pp.30-42, 2018.
[35]J. Henriques, F. Caldeira, T. Cruz, and P. Simões, “Combining k-means and xgboost models for anomaly detection using log datasets,” Electronics, vol.9 no.7, p.1164, 2020.
[36]A. Garg, W. Zhang, J. Samaran, R. Savitha, and C.S. Foo, “An evaluation of anomaly detection and diagnosis in multivariate time series,” IEEE Transactions on Neural Networks and Learning Systems, vol.33 no.6, pp.2508-2517, 2021.
[37]H. Zhang, and Z. Li, “Anomaly detection approach for urban sensing based on credibility and time-series analysis optimization model,” IEEE Access, vol.7, pp.49102-49110, 2019.
[38]W. Ullah, A. Ullah, I.U. Haq, K. Muhammad, M. Sajjad, and S.W. Baik, “CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks,” Multimed. Tools Appl, vol.80, pp.16979-16995, 2021.
[39]N. Li, and F. Chang, “Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder,” Neurocomputing, vol.369, pp.92-105, 2019.
[40]L. Feremans, V. Vercruyssen, W. Meert, B. Cule, and B. Goethals, “A framework for pattern mining and anomaly detection in multi-dimensional time series and event logs,” In New Frontiers in Mining Complex Patterns: 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers 8, pp. 3-20, Springer International Publishing, 2020.
[41]F. Jiang, J. Yuan, S.A. Tsaftaris, and A.K. Katsaggelos, “Anomalous video event detection using spatiotemporal context,” Com. Vis. Ima Unders, vol.115 no.3, pp.323-333, 2011.
[42]Q. Fu, J.G. Lou, Y. Wang, and J. Li, “Execution anomaly detection in distributed systems through unstructured log analysis,” In Proceedings of 2009 ninth IEEE international conference on data mining, pp. 149-158, IEEE, December 2009.
[43]I. Friedberg, F. Skopik, G.  Settanni, and R. Fiedler, “Combating advanced persistent threats: From network event correlation to incident detection,” Comput. Secur, vol.48, pp.35-57, 2015.
[44]H.K. Kim, K.H. Im, and S.C. Park, “DSS for computer security incident response applying CBR and collaborative response,” Expert Syst. Appl, vol.37 no.1, pp.852-870, 2010.
[45]Z. Lijun, H. Guiqiu, L. Qingsheng, and D. Guanhua, “An intuitionistic calculus to complex abnormal event recognition on data streams,” Secur Commun Netw, pp.1-14, 2021.
[46]P. Radoglou-Grammatikis, P. Sarigiannidis, E. Iturbe, E. Rios, S. Martinez, A. Sarigiannidis, G. Eftathopoulos, Y. Spyridis, A. Sesis, N. Vakakis, and D. Tzovaras, “Spear siem: A security information and event management system for the smart grid,” Com. Net, vol.193, pp.108008., 2021. 
[47]M. Saebi, J. Xu, L.M. Kaplan, B. Ribeiro, and N.V. Chawla, “Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection,” EPJ Data Sci, vol.9 no.1, p.15, 2020.
[48]Y.S. Chong, and Y.H. Tay, “Abnormal event detection in videos using spatiotemporal autoencoder,” In Advances in Neural Networks-ISNN 2017: 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21–26, 2017, Proceedings, Part II 14, pp. 189-196, Springer International Publishing, 2017.
[49]R.V.H.M. Colque, C. Caetano, M.T.L. de Andrade, and W.R. Schwartz, “Histograms of optical flow orientation and magnitude and entropy to detect anomalous events in videos”, IEEE Transactions on Circuits and Systems for Video Technology, vol.27 no.3, pp.673-682, 2016.
[50]L.J. Chen, Y.H. Ho, H.H. Hsieh, S.T. Huang, H.C.  Lee, and S. Mahajan, “ADF: An anomaly detection framework for large-scale PM2. 5 sensing systems,” IEEE Internet of Things Journal, vol.5 no.2, pp.559-570, 2017.
[51]S. Ahmad, A. Lavin, S. Purdy, and Z. Agha, “Unsupervised real-time anomaly detection for streaming data,” Neurocomputing, vol. 262, pp.134-147, 2017.
[52]N. Cao, C. Shi, S. Lin, J. Lu, Y.R. Lin, and C.Y. Lin, “Targetvue: Visual analysis of anomalous user behaviors in online communication systems,” IEEE Transactions on Visualization and computer graphics, vol.22, no.1, pp.280-289, 2015.
[53]X. Xi, T. Zhang, D. Du, G. Zhao, Q. Gao, W. Zhao, and S. Zhang, “Method and System for Detecting Anomalous User Behaviors: An Ensemble Approach,” In SEKE, pp. 263-262, 2018.
[54]T. Laue, T. Klecker, C. Kleiner, and K.O. Detken, “A SIEM Architecture for Advanced Anomaly Detection,” Open Journal of Big Data, vol.6 no.1, pp.26-42, 2022.
[55]V. Muliukha, A. Lukashin, L.V. Utkin, M. Popov, and A.A. Meldo, “Anomaly Detection Approach in Cyber Security for User and Entity Behavior Analytics System,” In ESANN, pp. 251-256, October 2020.