ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection
Anomaly detection, crucial for identifying issues such as financial fraud or medical malfunctions, has advanced significantly with machine learning (ML) and deep learning (DL). However, a major problem in the field is that no single model works best with diverse datasets and problem domains. To addr...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10819404/ |
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| author | Nadia Rashid Rashid Mehmood Fahad Alqurashi Saad Alqahtany Juan M. Corchado |
| author_facet | Nadia Rashid Rashid Mehmood Fahad Alqurashi Saad Alqahtany Juan M. Corchado |
| author_sort | Nadia Rashid |
| collection | DOAJ |
| description | Anomaly detection, crucial for identifying issues such as financial fraud or medical malfunctions, has advanced significantly with machine learning (ML) and deep learning (DL). However, a major problem in the field is that no single model works best with diverse datasets and problem domains. To address this, we propose an innovative auto-selective approach and software tool based on meta-learning, called ASAD (Auto-Selective Anomaly Detection), to dynamically select the most appropriate model based on the unique features of a given dataset or problem domain. ASAD trains an ML model to predict the best candidate from a large pool of models by considering the specific characteristics and requirements of the dataset. It is trained using 139 datasets built upon 60 base datasets from 11 diverse domains (finance, healthcare, network security) and 80 ML and DL models composed of 22 base anomaly detection algorithms. It uses meta-features and correlation functions to evaluate 300 features. It selects the best-performing model, evaluates models based on 7 metrics, and delivers significantly better performance than the cutting-edge in this area. ASAD addresses the critical challenge of model generalization and adaptability, aiming to enhance the efficiency and accuracy of anomaly detection across varied application domains. By automating the selection process, the method aims to reduce the reliance on trial-and-error methods, streamline the anomaly detection workflow, and lead to more robust, adaptable, and efficient anomaly detection systems. We provide the methodology, implementation, and evaluation of our approach, offering insights into its potential to revolutionize anomaly detection in this era characterized by vast and complex datasets. |
| format | Article |
| id | doaj-art-d9b4706487fa442e9b41c5cce15b87d0 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-d9b4706487fa442e9b41c5cce15b87d02025-01-10T00:00:48ZengIEEEIEEE Access2169-35362025-01-01134341436710.1109/ACCESS.2024.352490810819404ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly DetectionNadia Rashid0https://orcid.org/0009-0001-2560-4017Rashid Mehmood1https://orcid.org/0000-0002-4997-5322Fahad Alqurashi2https://orcid.org/0000-0002-7919-747XSaad Alqahtany3Juan M. Corchado4Department of Computer Science, FCIT, King Abdulaziz University, Jeddah, Saudi ArabiaFaculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaDepartment of Computer Science, FCIT, King Abdulaziz University, Jeddah, Saudi ArabiaFaculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaBISITE Research Group, University of Salamanca, Salamanca, SpainAnomaly detection, crucial for identifying issues such as financial fraud or medical malfunctions, has advanced significantly with machine learning (ML) and deep learning (DL). However, a major problem in the field is that no single model works best with diverse datasets and problem domains. To address this, we propose an innovative auto-selective approach and software tool based on meta-learning, called ASAD (Auto-Selective Anomaly Detection), to dynamically select the most appropriate model based on the unique features of a given dataset or problem domain. ASAD trains an ML model to predict the best candidate from a large pool of models by considering the specific characteristics and requirements of the dataset. It is trained using 139 datasets built upon 60 base datasets from 11 diverse domains (finance, healthcare, network security) and 80 ML and DL models composed of 22 base anomaly detection algorithms. It uses meta-features and correlation functions to evaluate 300 features. It selects the best-performing model, evaluates models based on 7 metrics, and delivers significantly better performance than the cutting-edge in this area. ASAD addresses the critical challenge of model generalization and adaptability, aiming to enhance the efficiency and accuracy of anomaly detection across varied application domains. By automating the selection process, the method aims to reduce the reliance on trial-and-error methods, streamline the anomaly detection workflow, and lead to more robust, adaptable, and efficient anomaly detection systems. We provide the methodology, implementation, and evaluation of our approach, offering insights into its potential to revolutionize anomaly detection in this era characterized by vast and complex datasets.https://ieeexplore.ieee.org/document/10819404/Anomaly detectiondeep learningfinancial fraud detectionhealthcare anomaliesmachine learningmeta-features |
| spellingShingle | Nadia Rashid Rashid Mehmood Fahad Alqurashi Saad Alqahtany Juan M. Corchado ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection IEEE Access Anomaly detection deep learning financial fraud detection healthcare anomalies machine learning meta-features |
| title | ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection |
| title_full | ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection |
| title_fullStr | ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection |
| title_full_unstemmed | ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection |
| title_short | ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection |
| title_sort | asad a meta learning based auto selective approach and tool for anomaly detection |
| topic | Anomaly detection deep learning financial fraud detection healthcare anomalies machine learning meta-features |
| url | https://ieeexplore.ieee.org/document/10819404/ |
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