A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data Article Swipe
Redhwan Al-amri
,
Raja Kumar Murugesan
,
Mustafa Man
,
Alaa Fareed
,
Mohammed A. Al‐Sharafi
,
Ammar Ahmed Alkahtani
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app11125320
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app11125320
Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/app11125320
- https://www.mdpi.com/2076-3417/11/12/5320/pdf?version=1623138299
- OA Status
- gold
- Cited By
- 161
- References
- 80
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3168992578
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3168992578Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app11125320Digital Object Identifier
- Title
-
A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT DataWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-08Full publication date if available
- Authors
-
Redhwan Al-amri, Raja Kumar Murugesan, Mustafa Man, Alaa Fareed, Mohammed A. Al‐Sharafi, Ammar Ahmed AlkahtaniList of authors in order
- Landing page
-
https://doi.org/10.3390/app11125320Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/11/12/5320/pdf?version=1623138299Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/11/12/5320/pdf?version=1623138299Direct OA link when available
- Concepts
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Computer science, Anomaly detection, Data stream mining, Data stream, Data science, Data mining, Downtime, Machine learning, Artificial intelligence, Operating system, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
161Total citation count in OpenAlex
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2025: 40, 2024: 58, 2023: 34, 2022: 22, 2021: 7Per-year citation counts (last 5 years)
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80Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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