Abstract
Anomaly detection is a popular research topic these days since it influences a wide scope of uses. A variety of strategies for detecting abnormalities in various domains have been developed. The most appropriate anomaly detection technique depends on the problem characteristics and problem domain, which is why this chapter focuses on extremely common strategies for recognizing abnormalities in many application areas, namely machine learning techniques. These strategies try to make a system intelligent, allowing it to adopt new things from data sets and readily classify important information from abnormalities. This chapter also addresses the benefits and drawbacks of machine learning approaches, as well as the different types of anomaly detection applications.
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Chaudhary, A., Agarwal, R. (2023). Machine Learning Techniques for Anomaly Detection Application Domains. In: Rai, A., Kumar Singh, D., Sehgal, A., Cengiz, K. (eds) Paradigms of Smart and Intelligent Communication, 5G and Beyond. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-99-0109-8_8
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DOI: https://doi.org/10.1007/978-981-99-0109-8_8
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