Abstract
Anomaly detection can be defined as the process to find out the outliers of a dataset, where outliers or anomalies are the instances that don’t belong to that particular dataset. These anomalies might point to a wide range of things such as unusual network traffic, uncover a faulty sensor, or simply identify data for cleaning, before analysis, etc. Detecting anomalous behavior beforehand can prevent malignant abuse, data breaches, and intellectual property theft. Various anomaly detection techniques have recently become available and commonly used. Based on availability of techniques, one can yield better results for a specific user or dataset than others. This paper provides a concise overview of the most broadly used strategies for detecting anomalies. For this purpose, we present recent research works briefly, along with their established methodology. Finally, we outline some challenges to be dealt with while working with these methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACMÂ Comput. Surv. 41(3) (2009)
Aggarwal, C.C.: Outlier Ensembles: Position Paper. ACM SIGKDD Explorations Newsletter. 14(2) (2013)
Gogoi, P., Bhattacharyya, D., Borah, B., Kalita, J.K.: A survey of outlier detection methods in network anomaly identification. Comput. J. 54(4), 570–588 (2011)
Gupta, M., Gao, J., Aggarwal, C.C., Han, J.: Outlier Detection for Temporal Data: A Survey. IEEE T. Knowl. Data En. 26(9), 2250–2267 (2014)
Ren, H., et al.: Time-series anomaly detection service at microsoft. In: 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)
Siffer, A., Fouque, P.A., Termier, A., Largouet, C.: anomaly detection in streams with extreme value theory. In: 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017)
Ezeme, O.M., Mahmoud, Q.H., Azim, A.: A framework for anomaly detection in time-driven and event-driven processes using kernel traces. IEEE T. Knowl. Data En. (2020)
Liu, F., et al.: Anomaly detection in quasi-periodic time series based on automatic data segmentation and attentional LSTM-CNN. IEEE T. Knowl. Data En. (2020)
Maya, S., Ueno, K., Nishikawa, T.: dLSTM: a new approach for anomaly detection using deep learning with delayed prediction. Int. J. Data Sci. Anal. 8(2), 137–164 (2019). https://doi.org/10.1007/s41060-019-00186-0
M¨unz, G., Li, S., Carle, G.: Traffic anomaly detection using K-means clustering. In: GI/ITG-Workshop MMBnet (2007)
Yasami, Y., Mozaffari, S.P.: A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning methods. J. Supercomput. 53, 231–245 (2010)
Pang, G., Shen, C., Hengel, A.: Deep anomaly detection with deviation networks. In: 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)
Jidiga, G.R., Sammulal, P.: Anomaly detection using machine learning with a case study. In: IEEE International Conference on Advanced Communications, Control and Computing Technologies (2014)
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017)
Lim, S.K., et al.: DOPING: generative data augmentation for unsupervised anomaly detection with GAN. In: IEEE International Conference on Data Mining (2018)
Zenati, H., Romain, M., Foo, C.S., Lecouat, B., Chandrasekhar, V.: Adversarially learned anomaly detection. In: IEEE International Conference on Data Mining (2018)
Tsou, Y.L., Chu, H.M., Li, C., Yang, S.W.: Robust distributed anomaly detection using optimal weighted one-class random forests. In: IEEE International Conference on Data Mining (2018)
Ramakrishnan, J., Shaabani, E., Li, C., Sustik, M.A.: Anomaly detection for an e-commerce pricing system. In: 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)
Oh, M., Iyengar, G.: Sequential anomaly detection using ınverse reinforcement learning. In: 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)
Chitrakar, R., Chuanhe, H.: Anomaly detection using support vector machine classification with k-Medoids clustering. In: 3rd Asian Himalayas International Conference on Internet (2012)
Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: 32nd Conference on Neural Information Processing Systems (2018)
Vercruyssen, V., Meert, W., Verbruggen, G., Maes, K., Baumer, R., Davis, J.: Semi-supervised anomaly detection with an application to water analytics. In: IEEE International Conference on Data Mining (2018)
Ranjbar, V., Salehi, M., Jandaghi, P., Jalili, M.: QANet: tensor decomposition approach for query-based anomaly detection in heterogeneous information networks. IEEE T. Knowl. Data En. 31(11), 2178–2189 (2019)
Peng, Z., Luo, M., Li, J., Xue, L., Zheng, Q.: A deep multi-view framework for anomaly detection on attributed networks. IEEE T. Knowl. Data En. 14(8) (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Karim, R., Rizvi, M.A.I., Arefin, M.S. (2022). A Survey on Anomaly Detection Strategies. In: Chen, J.IZ., Tavares, J.M.R.S., Iliyasu, A.M., Du, KL. (eds) Second International Conference on Image Processing and Capsule Networks. ICIPCN 2021. Lecture Notes in Networks and Systems, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-84760-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-84760-9_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84759-3
Online ISBN: 978-3-030-84760-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)