{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T11:06:16Z","timestamp":1782644776547,"version":"3.54.5"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Te Hiranga R\u016b QuakeCoRE, an Aotearoa New Zealand Tertiary Education Commission-funded Centre","award":["1068"],"award-info":[{"award-number":["1068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Time series anomaly detection in streaming environments faces persistent challenges due to concept drift, which gradually degrades model reliability. In this paper, we propose Anomaly Detection with Drift-Aware Ensemble-based Incremental Learning (ADDAEIL), an unsupervised anomaly detection framework that incrementally adapts to concept drift in non-stationary streaming time series data. ADDAEIL integrates a hybrid drift detection mechanism that combines statistical distribution tests with structural-based performance evaluation of base detectors in Isolation Forest. This design enables unsupervised detection and continuous adaptation to evolving data patterns. Based on the estimated drift intensity, an adaptive update strategy selectively replaces degraded base detectors. This allows the anomaly detection model to incorporate new information while preserving useful historical behavior. Experiments on both real-world and synthetic datasets show that ADDAEIL consistently outperforms existing state-of-the-art methods and maintains robust long-term performance in non-stationary data streams.<\/jats:p>","DOI":"10.3390\/a18060359","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T08:18:06Z","timestamp":1749629886000},"page":"359","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6578-2241","authenticated-orcid":false,"given":"Danlei","family":"Li","sequence":"first","affiliation":[{"name":"Department of Electrical, Computer, and Software Engineering, The University of Auckland, 20 Symonds Street, Auckland CBD, Auckland 1010, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8456-3999","authenticated-orcid":false,"given":"Nirmal-Kumar C.","family":"Nair","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer, and Software Engineering, The University of Auckland, 20 Symonds Street, Auckland CBD, Auckland 1010, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8450-2558","authenticated-orcid":false,"given":"Kevin I-Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer, and Software Engineering, The University of Auckland, 20 Symonds Street, Auckland CBD, Auckland 1010, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","article-title":"Anomaly Detection for IoT Time-Series Data: A Survey","volume":"7","author":"Cook","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_3","first-page":"2346","article-title":"Learning under Concept Drift: A Review","volume":"31","author":"Lu","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, J., Malialis, K., Panayiotou, C.G., and Polycarpou, M.M. (July, January 30). Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences. Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan.","DOI":"10.1109\/IJCNN60899.2024.10649991"},{"key":"ref_5","first-page":"387","article-title":"Mgfn: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection","volume":"37","author":"Chen","year":"2023","journal-title":"AAAI Conf. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1109\/TNN.2011.2160459","article-title":"Incremental learning of concept drift in nonstationary environments","volume":"22","author":"Elwell","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3955","DOI":"10.1007\/s13042-024-02492-x","article-title":"Deepstreamensemble: Streaming adaptation to concept drift in deep neural networks","volume":"16","author":"Chambers","year":"2025","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_8","first-page":"5533777","article-title":"Recurrent adaptive classifier ensemble for handling recurring concept drifts","volume":"2021","author":"Museba","year":"2021","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2133360.2133363","article-title":"Isolation-Based Anomaly Detection","volume":"6","author":"Liu","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10994-015-5521-0","article-title":"Loda: Lightweight on-line detector of anomalies","volume":"102","year":"2016","journal-title":"Mach. Learn."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bhatia, S., Jain, A., Srivastava, S., Kawaguchi, K., and Hooi, B. (2022, January 25\u201329). Memstream: Memory-based streaming anomaly detection. Proceedings of the ACM Web Conference 2022, Virtual.","DOI":"10.1145\/3485447.3512221"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yang, L., Manias, D.M., and Shami, A. (2021, January 7\u201311). Pwpae: An ensemble framework for concept drift adaptation in iot data streams. Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain.","DOI":"10.1109\/GLOBECOM46510.2021.9685338"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108632","DOI":"10.1016\/j.knosys.2022.108632","article-title":"From concept drift to model degradation: An overview on performance-aware drift detectors","volume":"245","author":"Bayram","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9523","DOI":"10.1016\/j.jksuci.2021.11.006","article-title":"Concept drift detection in data stream mining: A literature review","volume":"34","author":"Agrahari","year":"2022","journal-title":"J. King Saud-Univ.-Comput. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, D., Nair, N., Kevin, I., Wang, K., and Sakurai, K. (2024, January 2\u20137). Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data. Proceedings of the 2024 IEEE Smart World Congress (SWC), Nadi, Fiji.","DOI":"10.1109\/SWC62898.2024.00091"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"12181","DOI":"10.1109\/TKDE.2022.3159580","article-title":"Ecod: Unsupervised outlier detection using empirical cumulative distribution functions","volume":"35","author":"Li","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111002","DOI":"10.1016\/j.knosys.2023.111002","article-title":"A novel unsupervised framework for time series data anomaly detection via spectrum decomposition","volume":"280","author":"Lei","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lin, S., Clark, R., Birke, R., Sch\u00f6nborn, S., Trigoni, N., and Roberts, S. (2020, January 4\u20138). Anomaly detection for time series using vae-lstm hybrid model. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053558"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lai, K.H., Wang, L., Chen, H., Zhou, K., Wang, F., Yang, H., and Hu, X. (2023, January 27\u201329). Context-aware domain adaptation for time series anomaly detection. Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), Paul Twin Cities, MN, USA.","DOI":"10.1137\/1.9781611977653.ch76"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gama, J., Medas, P., Castillo, G., and Rodrigues, P. (October, January 29). Learning with drift detection. Proceedings of the Advances in Artificial Intelligence\u2013SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Brazil. Proceedings 17.","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bifet, A., and Gavalda, R. (2007, January 26\u201328). Learning from time-changing data with adaptive windowing. Proceedings of the 2007 SIAM International Conference on Data Mining, Minneapolis, MN, USA.","DOI":"10.1137\/1.9781611972771.42"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102365","DOI":"10.1016\/j.datak.2024.102365","article-title":"State-transition-aware anomaly detection under concept drifts","volume":"154","author":"Li","year":"2024","journal-title":"Data Knowl. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2523813","article-title":"A survey on concept drift adaptation","volume":"46","author":"Gama","year":"2014","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Berger, V.W., and Zhou, Y. (2014). Kolmogorov\u2013smirnov test: Overview. Wiley Statsref: Statistics Reference Online, Wiley.","DOI":"10.1002\/9781118445112.stat06558"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lovric, M. (2011). Kullback-Leibler Divergence. International Encyclopedia of Statistical Science, Springer.","DOI":"10.1007\/978-3-642-04898-2"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"MacFarland, T.W., and Yates, J.M. (2016). Mann\u2013Whitney U Test. Introduction to Nonparametric Statistics for the Biological Sciences Using R, Springer.","DOI":"10.1007\/978-3-319-30634-6"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s10115-018-1257-z","article-title":"Survey of distance measures for quantifying concept drift and shift in numeric data","volume":"60","author":"Goldenberg","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4351","DOI":"10.1007\/s10994-022-06188-7","article-title":"STUDD: A student\u2013teacher method for unsupervised concept drift detection","volume":"112","author":"Cerqueira","year":"2023","journal-title":"Mach. Learn."},{"key":"ref_29","first-page":"2755","article-title":"Dynamic weighted majority: An ensemble method for drifting concepts","volume":"8","author":"Kolter","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Korycki, L., and Krawczyk, B. (2019, January 5\u20138). Unsupervised drift detector ensembles for data stream mining. Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Washington, DC, USA.","DOI":"10.1109\/DSAA.2019.00047"},{"key":"ref_31","unstructured":"Laptev, N., and Amizadeh, S. (2025, May 06). Yahoo Anomaly Detection Dataset S5. Available online: https:\/\/www.researchgate.net\/publication\/336315311_Unsupervised_Drift_Detector_Ensembles_for_Data_Stream_Mining."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lavin, A., and Ahmad, S. (2015, January 9\u201311). Evaluating real-time anomaly detection algorithms\u2013the Numenta anomaly benchmark. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.141"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101672","DOI":"10.1016\/j.ecoinf.2022.101672","article-title":"Detecting outliers in a univariate time series dataset using unsupervised combined statistical methods: A case study on surface water temperature","volume":"69","author":"Jamshidi","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.neucom.2017.04.070","article-title":"Unsupervised real-time anomaly detection for streaming data","volume":"262","author":"Ahmad","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.neucom.2020.04.077","article-title":"Online anomaly detection with sparse Gaussian processes","volume":"403","author":"Gu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"120994","DOI":"10.1016\/j.eswa.2023.120994","article-title":"Anomaly detection in streaming data: A comparison and evaluation study","volume":"233","author":"Hartl","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1007\/s11063-022-11015-0","article-title":"Concept drift adaptation for time series anomaly detection via transformer","volume":"55","author":"Ding","year":"2023","journal-title":"Neural Process. Lett."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/6\/359\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:50:06Z","timestamp":1760032206000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/6\/359"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,11]]},"references-count":37,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["a18060359"],"URL":"https:\/\/doi.org\/10.3390\/a18060359","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,11]]}}}