{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:01:30Z","timestamp":1777982490602,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001778","name":"Deakin University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001778","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Anomaly detection in streaming data is an important task for many real-world applications, such as network security, fraud detection, and system monitoring. However, streaming data often exhibit concept drift, which means that the data distribution changes over time. This poses a significant challenge for many anomaly detection algorithms, as they need to adapt to the evolving data to maintain high detection accuracy. Existing streaming anomaly detection algorithms lack a unified evaluation framework that validly assesses their performance and robustness under different types of concept drifts and anomalies. In this paper, we conduct a systematic technical review of the state-of-the-art methods for anomaly detection in streaming data. We propose a new data generator, called\n                    <jats:italic>SCAR<\/jats:italic>\n                    (\n                    <jats:bold>S<\/jats:bold>\n                    treaming data generator with\n                    <jats:bold>C<\/jats:bold>\n                    ustomizable\n                    <jats:bold>A<\/jats:bold>\n                    nomalies and concept d\n                    <jats:bold>R<\/jats:bold>\n                    ifts), that can synthesize streaming data based on synthetic and real-world datasets from different domains. Furthermore, we adapt four static anomaly detection models to the streaming setting using a generic reconstruction strategy as baselines, and then compare them systematically with 9 existing streaming anomaly detection algorithms on 76 synthesized datasets that have various types of anomalies and concept drifts. The challenges and future research directions for anomaly detection in streaming data are also presented.\n                  <\/jats:p>","DOI":"10.1007\/s10462-024-10995-w","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T04:06:39Z","timestamp":1730952399000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Revisiting streaming anomaly detection: benchmark and evaluation"],"prefix":"10.1007","volume":"58","author":[{"given":"Yang","family":"Cao","sequence":"first","affiliation":[]},{"given":"Yixiao","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Ye","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Kai Ming","family":"Ting","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"10995_CR1","doi-asserted-by":"crossref","unstructured":"Angiulli F, Fassetti F (2007) Detecting distance-based outliers in streams of data. In: Proceedings of the 16th ACM conference on conference on information and knowledge management, pp 811\u2013820","DOI":"10.1145\/1321440.1321552"},{"key":"10995_CR6","doi-asserted-by":"crossref","unstructured":"Bandaragoda TR, Ting KM, Albrecht D, Liu FT, Wells JR (2014) Efficient anomaly detection by isolation using nearest neighbour ensemble. In: 2014 IEEE international conference on data mining workshop. IEEE, pp 698\u2013705","DOI":"10.1109\/ICDMW.2014.70"},{"key":"10995_CR3","doi-asserted-by":"crossref","unstructured":"Bhatia S, Jain A, Li P, Kumar R, Hooi B (2021) MSTREAM: fast anomaly detection in multi-aspect streams. In: Proceedings of the web conference 2021, pp 3371\u20133382","DOI":"10.1145\/3442381.3450023"},{"key":"10995_CR4","doi-asserted-by":"crossref","unstructured":"Bhatia S, Jain A, Srivastava S, Kawaguchi K, Hooi B (2022) MEMSTREAM: memory-based streaming anomaly detection. In: Proceedings of the ACM web conference 2022, pp. 610\u2013621","DOI":"10.1145\/3485447.3512221"},{"key":"10995_CR2","unstructured":"Bifet A, Holmes G, Pfahringer B, Kranen P, Kremer H, Jansen T, Seidl T (2010) MOA: massive online analysis, a framework for stream classification and clustering. In: Proceedings of the 1st workshop on applications of pattern analysis, pp 44\u201350 (2010)"},{"issue":"3","key":"10995_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3381028","volume":"53","author":"A Boukerche","year":"2020","unstructured":"Boukerche A, Zheng L, Alfandi O (2020) Outlier detection: Methods, models, and classification. ACM Comput Surv (CSUR) 53(3):1\u201337","journal-title":"ACM Comput Surv (CSUR)"},{"key":"10995_CR5","doi-asserted-by":"crossref","unstructured":"Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data, pp 93\u2013104","DOI":"10.1145\/342009.335388"},{"key":"10995_CR9","doi-asserted-by":"crossref","unstructured":"Cao L, Yang D, Wang Q, Yu Y, Wang J, Rundensteiner EA (2014) Scalable distance-based outlier detection over high-volume data streams. In: 2014 IEEE 30th international conference on data engineering. IEEE, pp 76\u201387","DOI":"10.1109\/ICDE.2014.6816641"},{"issue":"3","key":"10995_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3):1\u201358","journal-title":"ACM Comput Surv (CSUR)"},{"key":"10995_CR13","doi-asserted-by":"crossref","unstructured":"Dau HA, Keogh E, Kamgar K, Yeh C-CM, Zhu Y, Gharghabi S, Ratanamahatana CA, Chen Y, Hu B, Begum N, Bagnall A, Mueen A, Batista G (2018) Hexagon-ML: the UCR time series classification archive. https:\/\/www.cs.ucr.edu\/~eamonn\/time_series_data_2018\/","DOI":"10.1109\/JAS.2019.1911747"},{"key":"10995_CR10","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"issue":"20","key":"10995_CR11","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3182\/20130902-3-CN-3020.00044","volume":"46","author":"Z Ding","year":"2013","unstructured":"Ding Z, Fei M (2013) An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window. IFAC Proc Vol 46(20):12\u201317","journal-title":"IFAC Proc Vol"},{"key":"10995_CR12","unstructured":"Dua D, Graff C (2017) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"issue":"4","key":"10995_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama J, \u017dliobait\u0117 I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv (CSUR) 46(4):1\u201337","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"2","key":"10995_CR15","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/s10115-018-1257-z","volume":"60","author":"I Goldenberg","year":"2019","unstructured":"Goldenberg I, Webb GI (2019) Survey of distance measures for quantifying concept drift and shift in numeric data. Knowl Inf Syst 60(2):591\u2013615","journal-title":"Knowl Inf Syst"},{"key":"10995_CR14","unstructured":"Guha S, Mishra N, Roy G, Schrijvers O (2016) Robust random cut forest based anomaly detection on streams. In: International conference on machine learning, pp 2712\u20132721"},{"key":"10995_CR17","first-page":"32142","volume":"35","author":"S Han","year":"2022","unstructured":"Han S, Hu X, Huang H, Jiang M, Zhao Y (2022a) ADBench: anomaly detection benchmark. Adv Neural Inf Process Syst 35:32142\u201332159","journal-title":"Adv Neural Inf Process Syst"},{"key":"10995_CR18","doi-asserted-by":"crossref","unstructured":"Han X, Zhu Y, Ting KM, Zhan D-C, Li G (2022b) Streaming hierarchical clustering based on point-set kernel. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 525\u2013533","DOI":"10.1145\/3534678.3539323"},{"key":"10995_CR19","doi-asserted-by":"crossref","unstructured":"Kontaki M, Gounaris A, Papadopoulos AN, Tsichlas K, Manolopoulos Y (2011) Continuous monitoring of distance-based outliers over data streams. In: 2011 IEEE 27th international conference on data engineering. IEEE, pp 135\u2013146","DOI":"10.1109\/ICDE.2011.5767923"},{"key":"10995_CR20","doi-asserted-by":"crossref","unstructured":"Liu FT, Ting KM, Zhou Z-H (2008) Isolation forest. In: 2008 8th IEEE international conference on data mining. IEEE, pp 413\u2013422","DOI":"10.1109\/ICDM.2008.17"},{"issue":"10","key":"10995_CR21","doi-asserted-by":"publisher","first-page":"6353","DOI":"10.3390\/app13106353","volume":"13","author":"T Lu","year":"2023","unstructured":"Lu T, Wang L, Zhao X (2023) Review of anomaly detection algorithms for data streams. Appl Sci 13(10):6353","journal-title":"Appl Sci"},{"key":"10995_CR23","doi-asserted-by":"crossref","unstructured":"Manzoor E, Lamba H, Akoglu L (2018) Xstream: outlier detection in feature-evolving data streams. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1963\u20131972","DOI":"10.1145\/3219819.3220107"},{"issue":"7","key":"10995_CR22","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TKDE.2012.109","volume":"25","author":"MM Masud","year":"2012","unstructured":"Masud MM, Chen Q, Khan L, Aggarwal CC, Gao J, Han J, Srivastava A, Oza NC (2012) Classification and adaptive novel class detection of feature-evolving data streams. IEEE Trans Knowl Data Eng 25(7):1484\u20131497","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10995_CR25","doi-asserted-by":"crossref","unstructured":"Na GS, Kim D, Yu H (2018) DILOF: effective and memory efficient local outlier detection in data streams. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1993\u20132002","DOI":"10.1145\/3219819.3220022"},{"key":"10995_CR24","doi-asserted-by":"crossref","unstructured":"Ntroumpogiannis A, Giannoulis M, Myrtakis N, Christophides V, Simon E, Tsamardinos I: A meta-level analysis of online anomaly detectors. The VLDB Journal, 1\u201342 (2023)","DOI":"10.1007\/s00778-022-00773-x"},{"key":"10995_CR28","doi-asserted-by":"crossref","unstructured":"Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: a review. ACM Comput Surv (CSUR) 54(2):1\u201338","DOI":"10.1145\/3439950"},{"key":"10995_CR26","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s10994-015-5521-0","volume":"102","author":"T Pevn\u1ef3","year":"2016","unstructured":"Pevn\u1ef3 T (2016) LODA: lightweight on-line detector of anomalies. Mach Learn 102:275\u2013304","journal-title":"Mach Learn"},{"key":"10995_CR27","doi-asserted-by":"crossref","unstructured":"Pokrajac D, Lazarevic A, Latecki LJ (2007) Incremental local outlier detection for data streams. In: 2007 IEEE symposium on computational intelligence and data mining. IEEE, pp 504\u2013515","DOI":"10.1109\/CIDM.2007.368917"},{"key":"10995_CR29","unstructured":"Qin X, Cao L, Rundensteiner EA, Madden S (2019) Scalable kernel density estimation-based local outlier detection over large data streams. In: Proceedings of the 22nd international conference on extending database technology (EDBT)"},{"issue":"12","key":"10995_CR31","doi-asserted-by":"publisher","first-page":"3246","DOI":"10.1109\/TKDE.2016.2597833","volume":"28","author":"M Salehi","year":"2016","unstructured":"Salehi M, Leckie C, Bezdek JC, Vaithianathan T, Zhang X (2016) Fast memory efficient local outlier detection in data streams. IEEE Trans Knowl Data Eng 28(12):3246\u20133260","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10995_CR30","doi-asserted-by":"crossref","unstructured":"Sathe S, Aggarwal CC (2016) Subspace outlier detection in linear time with randomized hashing. In: 2016 IEEE 16th international conference on data mining (ICDM). IEEE, pp 459\u2013468","DOI":"10.1109\/ICDM.2016.0057"},{"key":"10995_CR34","unstructured":"Tan SC, Ting KM, Liu TF (2011) Fast anomaly detection for streaming data. In: Twenty-second international joint conference on artificial intelligence. Citeseer"},{"issue":"11","key":"10995_CR32","doi-asserted-by":"publisher","first-page":"2321","DOI":"10.14778\/3551793.3551796","volume":"15","author":"KM Ting","year":"2022","unstructured":"Ting KM, Liu Z, Zhang H, Zhu Y (2022) A new distributional treatment for time series and an anomaly detection investigation. Proc VLDB Endow 15(11):2321\u20132333","journal-title":"Proc VLDB Endow"},{"issue":"2","key":"10995_CR33","doi-asserted-by":"publisher","first-page":"141","DOI":"10.14778\/3425879.3425885","volume":"14","author":"L Tran","year":"2020","unstructured":"Tran L, Mun MY, Shahabi C (2020) Real-time distance-based outlier detection in data streams. Proc VLDB Endow 14(2):141\u2013153","journal-title":"Proc VLDB Endow"},{"key":"10995_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120994","volume":"233","author":"FI V\u00e1zquez","year":"2023","unstructured":"V\u00e1zquez FI, Hartl A, Zseby T, Zimek A (2023) Anomaly detection in streaming data: a comparison and evaluation study. Expert Syst Appl 233:120994","journal-title":"Expert Syst Appl"},{"issue":"4","key":"10995_CR36","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1007\/s10618-015-0448-4","volume":"30","author":"GI Webb","year":"2016","unstructured":"Webb GI, Hyde R, Cao H, Nguyen HL, Petitjean F (2016) Characterizing concept drift. Data Min Knowl Disc 30(4):964\u2013994","journal-title":"Data Min Knowl Disc"},{"issue":"5","key":"10995_CR37","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1007\/s11280-022-01052-z","volume":"25","author":"H Xiang","year":"2022","unstructured":"Xiang H, Zhang X (2022) Edge computing empowered anomaly detection framework with dynamic insertion and deletion schemes on data streams. World Wide Web 25(5):2163\u20132183","journal-title":"World Wide Web"},{"key":"10995_CR38","unstructured":"Yilmaz SF, Kozat SS (2020) PYSAD: a streaming anomaly detection framework in python. arXiv preprint. arXiv:2009.02572"},{"issue":"11","key":"10995_CR39","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.14778\/3342263.3342269","volume":"12","author":"S Yoon","year":"2019","unstructured":"Yoon S, Lee J-G, Lee BS (2019) NETS: extremely fast outlier detection from a data stream via set-based processing. Proc VLDB Endow 12(11):1303\u20131315","journal-title":"Proc VLDB Endow"},{"key":"10995_CR40","doi-asserted-by":"crossref","unstructured":"Yoon S, Lee J-G, Lee BS (2020) Ultrafast local outlier detection from a data stream with stationary region skipping. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1181\u20131191","DOI":"10.1145\/3394486.3403171"},{"key":"10995_CR41","doi-asserted-by":"crossref","unstructured":"Yoon S, Lee Y, Lee J-G, Lee BS (2022) Adaptive model pooling for online deep anomaly detection from a complex evolving data stream. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 2347\u20132357","DOI":"10.1145\/3534678.3539348"},{"issue":"4","key":"10995_CR42","doi-asserted-by":"publisher","first-page":"794","DOI":"10.14778\/3636218.3636233","volume":"17","author":"J Zhu","year":"2023","unstructured":"Zhu J, Cai S, Deng F, Ooi BC, Zhang W (2023) METER: a dynamic concept adaptation framework for online anomaly detection. Proc VLDB Endow 17(4):794\u2013807","journal-title":"Proc VLDB Endow"},{"key":"10995_CR44","doi-asserted-by":"crossref","unstructured":"Zhuang Z, Ting KM, Pang G, Song S (2023) Subgraph centralization: a necessary step for graph anomaly detection. In: Proceedings of the 2023 SIAM international conference on data mining (SDM). SIAM, pp 703\u2013711","DOI":"10.1137\/1.9781611977653.ch79"},{"key":"10995_CR43","unstructured":"\u017dliobait\u0117 I (2010) Learning under concept drift: an overview. arXiv preprint. arXiv:1010.4784"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10995-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10995-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10995-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T00:05:17Z","timestamp":1736553917000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10995-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["10995"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10995-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.170768490.03836281\/v1","asserted-by":"object"}]},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,7]]},"assertion":[{"value":"4 October 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"8"}}