Anomaly Detection in Data Streams Using Machine Learning and Deep Learning
Data stream mining for movement has emerged as an important area of machine learning because of the huge amount of changing and continuous data coming from diverse sources such as social media, business sensors, and mobile communications. The goal of this anomaly identification in the data streams...
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| Language: | English |
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International Transactions on Electrical Engineering and Computer Science
2025-01-01
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| Series: | International Transactions on Electrical Engineering and Computer Science |
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| Online Access: | https://iteecs.com/index.php/iteecs/article/view/118 |
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| author | Muhammad Amin Daneshwar |
| author_facet | Muhammad Amin Daneshwar |
| author_sort | Muhammad Amin Daneshwar |
| collection | DOAJ |
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Data stream mining for movement has emerged as an important area of machine learning because of the huge amount of changing and continuous data coming from diverse sources such as social media, business sensors, and mobile communications. The goal of this anomaly identification in the data streams is to find patterns that deviate substantially from the way things usually work. This will be valuable information for making decisions in a large number of areas, including healthcare, management of financial risk, keeping communities safe, and operating the power grid. This research discusses the intractable ways of finding oddities in a stream of data with the corresponding predicaments of always having a new inflow of data, creating information fast, and also dynamics of information changing. We also observe how distinct deep learning and machine learning approaches are being used in different fields to rapidly detect anomalies. Some examples of the way these techniques have been used to discover network intrusions, malware, IoT outliers, healthcare anomalies, and credit card frauds are a demonstration of the techniques work.
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| format | Article |
| id | doaj-art-bd27942c065d4a0981861c5dc2b7aff1 |
| institution | Kabale University |
| issn | 2583-6471 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | International Transactions on Electrical Engineering and Computer Science |
| record_format | Article |
| series | International Transactions on Electrical Engineering and Computer Science |
| spelling | doaj-art-bd27942c065d4a0981861c5dc2b7aff12025-01-02T09:31:35ZengInternational Transactions on Electrical Engineering and Computer ScienceInternational Transactions on Electrical Engineering and Computer Science2583-64712025-01-013410.62760/iteecs.3.4.2024.118Anomaly Detection in Data Streams Using Machine Learning and Deep LearningMuhammad Amin Daneshwar0Department of Computer Science and Engineering, Soran University, Soran, Iraq Data stream mining for movement has emerged as an important area of machine learning because of the huge amount of changing and continuous data coming from diverse sources such as social media, business sensors, and mobile communications. The goal of this anomaly identification in the data streams is to find patterns that deviate substantially from the way things usually work. This will be valuable information for making decisions in a large number of areas, including healthcare, management of financial risk, keeping communities safe, and operating the power grid. This research discusses the intractable ways of finding oddities in a stream of data with the corresponding predicaments of always having a new inflow of data, creating information fast, and also dynamics of information changing. We also observe how distinct deep learning and machine learning approaches are being used in different fields to rapidly detect anomalies. Some examples of the way these techniques have been used to discover network intrusions, malware, IoT outliers, healthcare anomalies, and credit card frauds are a demonstration of the techniques work. https://iteecs.com/index.php/iteecs/article/view/118Deep learningData stream miningAnomaly detectionNetwork intrusion detection systems |
| spellingShingle | Muhammad Amin Daneshwar Anomaly Detection in Data Streams Using Machine Learning and Deep Learning International Transactions on Electrical Engineering and Computer Science Deep learning Data stream mining Anomaly detection Network intrusion detection systems |
| title | Anomaly Detection in Data Streams Using Machine Learning and Deep Learning |
| title_full | Anomaly Detection in Data Streams Using Machine Learning and Deep Learning |
| title_fullStr | Anomaly Detection in Data Streams Using Machine Learning and Deep Learning |
| title_full_unstemmed | Anomaly Detection in Data Streams Using Machine Learning and Deep Learning |
| title_short | Anomaly Detection in Data Streams Using Machine Learning and Deep Learning |
| title_sort | anomaly detection in data streams using machine learning and deep learning |
| topic | Deep learning Data stream mining Anomaly detection Network intrusion detection systems |
| url | https://iteecs.com/index.php/iteecs/article/view/118 |
| work_keys_str_mv | AT muhammadamindaneshwar anomalydetectionindatastreamsusingmachinelearninganddeeplearning |