Discovering anomalies in big data: a review focused on the application of metaheuristics and machine learning techniques
With the increase in available data from computer systems and their security threats, interest in anomaly detection has increased as well in recent years. The need to diagnose faults and cyberattacks has also focused scientific research on the automated classification of outliers in big data, as man...
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Published in | Frontiers in big data Vol. 6; p. 1179625 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Frontiers Media S.A
17.08.2023
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Subjects | |
Online Access | Get full text |
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Summary: | With the increase in available data from computer systems and their security threats, interest in anomaly detection has increased as well in recent years. The need to diagnose faults and cyberattacks has also focused scientific research on the automated classification of outliers in big data, as manual labeling is difficult in practice due to their huge volumes. The results obtained from data analysis can be used to generate alarms that anticipate anomalies and thus prevent system failures and attacks. Therefore, anomaly detection has the purpose of reducing maintenance costs as well as making decisions based on reports. During the last decade, the approaches proposed in the literature to classify unknown anomalies in log analysis, process analysis, and time series have been mainly based on machine learning and deep learning techniques. In this study, we provide an overview of current state-of-the-art methodologies, highlighting their advantages and disadvantages and the new challenges. In particular, we will see that there is no absolute best method, i.e., for any given dataset a different method may achieve the best result. Finally, we describe how the use of metaheuristics within machine learning algorithms makes it possible to have more robust and efficient tools. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 Reviewed by: Guilherme De Alencar Barreto, Federal University of Ceara, Brazil; Erik Cuevas, University of Guadalajara, Mexico Edited by: A. Fong, Western Michigan University, United States |
ISSN: | 2624-909X 2624-909X |
DOI: | 10.3389/fdata.2023.1179625 |