Review of Anomaly Detection Algorithms for Data Streams
With the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams ubiquitous in all kinds of industries. Therefore,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/10/6353 |
| _version_ | 1827742150429769728 |
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| author | Tianyuan Lu Lei Wang Xiaoyong Zhao |
| author_facet | Tianyuan Lu Lei Wang Xiaoyong Zhao |
| author_sort | Tianyuan Lu |
| collection | DOAJ |
| description | With the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams ubiquitous in all kinds of industries. Therefore, detecting anomalies in such data streams could be very important and full of challenges. For example, in industries such as electricity and finance, data stream anomalies often contain information that can help avoiding risks and support decision making. However, most traditional anomaly detection algorithms rely on acquiring global information about the data, which is hard to apply to stream data scenarios. Currently, the reviews of the algorithm in the field of anomaly detection, both domestically and internationally, tend to focus on the exposition of anomaly detection algorithms in static data environments, while lacking in the induction and analysis of anomaly detection algorithms in the context of streaming data. As a result, unlike the existing literature reviews, this review provides the current mainstream anomaly detection algorithms in data streaming scenarios and categorizes them into three types on the basis of their fundamental principles: (1) based on offline learning; (2) based on semi-online learning; (3) based on online learning. This review discusses the current state of research on data stream anomaly detection and studies the key issues in various algorithms for detecting anomalies in data streams on the basis of concise summarization. Moreover, the review conducts a detailed comparison of the pros and cons of the algorithms. Finally, the future challenges in the field are analyzed, and future research directions are proposed. |
| first_indexed | 2024-03-11T03:58:08Z |
| format | Article |
| id | doaj.art-e7acc98cbb9b4cec950337551ecd87ea |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:58:08Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e7acc98cbb9b4cec950337551ecd87ea2023-11-18T00:24:27ZengMDPI AGApplied Sciences2076-34172023-05-011310635310.3390/app13106353Review of Anomaly Detection Algorithms for Data StreamsTianyuan Lu0Lei Wang1Xiaoyong Zhao2School of Information Management, Beijing Information Science and Technology University, Beijing 100192, ChinaSchool of Information Management, Beijing Information Science and Technology University, Beijing 100192, ChinaSchool of Information Management, Beijing Information Science and Technology University, Beijing 100192, ChinaWith the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams ubiquitous in all kinds of industries. Therefore, detecting anomalies in such data streams could be very important and full of challenges. For example, in industries such as electricity and finance, data stream anomalies often contain information that can help avoiding risks and support decision making. However, most traditional anomaly detection algorithms rely on acquiring global information about the data, which is hard to apply to stream data scenarios. Currently, the reviews of the algorithm in the field of anomaly detection, both domestically and internationally, tend to focus on the exposition of anomaly detection algorithms in static data environments, while lacking in the induction and analysis of anomaly detection algorithms in the context of streaming data. As a result, unlike the existing literature reviews, this review provides the current mainstream anomaly detection algorithms in data streaming scenarios and categorizes them into three types on the basis of their fundamental principles: (1) based on offline learning; (2) based on semi-online learning; (3) based on online learning. This review discusses the current state of research on data stream anomaly detection and studies the key issues in various algorithms for detecting anomalies in data streams on the basis of concise summarization. Moreover, the review conducts a detailed comparison of the pros and cons of the algorithms. Finally, the future challenges in the field are analyzed, and future research directions are proposed.https://www.mdpi.com/2076-3417/13/10/6353data streamsanomaly detectionmachine learningdeep learningonline learning |
| spellingShingle | Tianyuan Lu Lei Wang Xiaoyong Zhao Review of Anomaly Detection Algorithms for Data Streams Applied Sciences data streams anomaly detection machine learning deep learning online learning |
| title | Review of Anomaly Detection Algorithms for Data Streams |
| title_full | Review of Anomaly Detection Algorithms for Data Streams |
| title_fullStr | Review of Anomaly Detection Algorithms for Data Streams |
| title_full_unstemmed | Review of Anomaly Detection Algorithms for Data Streams |
| title_short | Review of Anomaly Detection Algorithms for Data Streams |
| title_sort | review of anomaly detection algorithms for data streams |
| topic | data streams anomaly detection machine learning deep learning online learning |
| url | https://www.mdpi.com/2076-3417/13/10/6353 |
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