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.
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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
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DOI: https://doi.org/10.1007/978-3-030-84760-9_25
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