{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T16:04:28Z","timestamp":1787673868651,"version":"build-2736575974"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1800702"],"award-info":[{"award-number":["2018YFB1800702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1800702"],"award-info":[{"award-number":["2018YFB1800702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1800702"],"award-info":[{"award-number":["2018YFB1800702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1800702"],"award-info":[{"award-number":["2018YFB1800702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1800702"],"award-info":[{"award-number":["2018YFB1800702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In the current era of information technology, blockchain is widely used in various fields, and the monitoring of the security and status of the blockchain system is of great concern. Online anomaly detection for the real-time stream data plays vital role in monitoring strategy to find abnormal events and status of blockchain system. However, as the high requirements of real-time and online scenario, online anomaly detection faces many problems such as limited training data, distribution drift, and limited update frequency. In this paper, we propose an adaptive stream outlier detection method (ASOD) to overcome the limitations. It first designs a K-nearest neighbor Gaussian mixture model (KNN-GMM) and utilizes online learning strategy. So, it is suitable for online scenarios and does not rely on large training data. The K-nearest neighbor optimization limits the influence of new data locally rather than globally, thus improving the stability. Then, ASOD applies the mechanism of dynamic maintenance of Gaussian components and the strategy of dynamic context control to achieve self-adaptation to the distribution drift. And finally, ASOD adopts a dimensionless distance metric based on Mahalanobis distance and proposes an automatic threshold method to accomplish anomaly detection. In addition, the KNN-GMM provides the life cycle and the anomaly index for continuous tracking and analysis, which facilities the cause analysis and further interpretation and traceability. From the experimental results, it can be seen that ASOD achieves near-optimal F1 and recall on the NAB dataset with an improvement of 6% and 20.3% over the average, compared to baselines with sufficient training data. ASOD has the lowest F1 variance among the five best methods, indicating that it is effective and stable for online anomaly detection on stream data.<\/jats:p>","DOI":"10.1186\/s13677-024-00682-0","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T14:26:36Z","timestamp":1720189596000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["ASOD: an adaptive stream outlier detection method using online strategy"],"prefix":"10.1186","volume":"13","author":[{"given":"Zhichao","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangzhan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Likun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haining","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"682_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.future.2022.01.017","volume":"131","author":"N Deepa","year":"2022","unstructured":"Deepa N, Pham QV, Nguyen DC, Bhattacharya S, Prabadevi B, Gadekallu TR et al (2022) A survey on blockchain for big data: approaches, opportunities, and future directions. Future Gener Comput Syst 131:209\u2013226","journal-title":"Future Gener Comput Syst"},{"key":"682_CR2","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jpdc.2020.06.008","volume":"145","author":"Y Mirsky","year":"2020","unstructured":"Mirsky Y, Golomb T, Elovici Y (2020) Lightweight collaborative anomaly detection for the IoT using blockchain. J Parallel Distrib Comput 145:75\u201397","journal-title":"J Parallel Distrib Comput"},{"issue":"1","key":"682_CR3","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/TNSE.2021.3068340","volume":"9","author":"J Du","year":"2022","unstructured":"Du J, Cheng W, Lu G, Cao H, Chu X, Zhang Z, Wang J (2022) Resource pricing and allocation in mec enabled blockchain systems: An a3c deep reinforcement learning approach. IEEE Trans Netw Sci Eng 9(1):33\u201344","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"682_CR4","doi-asserted-by":"crossref","unstructured":"Sayadi S, Rejeb SB, Choukair Z (2019) Anomaly detection model over blockchain electronic transactions. In 2019 15th international wireless communications & mobile computing conference (IWCMC). IEEE, p 895\u2013900","DOI":"10.1109\/IWCMC.2019.8766765"},{"key":"682_CR5","doi-asserted-by":"crossref","unstructured":"Zheng P, Zheng Z, Luo X, Chen X, Liu, X (2018) A detailed and real-time performance monitoring framework for blockchain systems. In Proceedings of the 40th international conference on software engineering: software engineering in practice, p 134\u2013143","DOI":"10.1145\/3183519.3183546"},{"key":"682_CR6","doi-asserted-by":"publisher","unstructured":"Lu T, Dai H, Wang B (2018) QoE-orientated resource allocation for wireless VR over small cell networks. In: 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP), pp 1\u20136. https:\/\/doi.org\/10.1109\/WCSP.2018.8555683","DOI":"10.1109\/WCSP.2018.8555683"},{"key":"682_CR7","doi-asserted-by":"crossref","unstructured":"Bogner A (2017) Seeing is understanding: anomaly detection in blockchains with visualized features. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. ACM, Maui Hawaii, pp 5\u20138","DOI":"10.1145\/3123024.3123157"},{"issue":"3","key":"682_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3501297","volume":"55","author":"J Soldani","year":"2022","unstructured":"Soldani J, Brogi A (2022) Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey. ACM Comput Surv (CSUR) 55(3):1\u201339","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"102","key":"682_CR9","first-page":"419","volume":"50","author":"MA Ferrag","year":"2020","unstructured":"Ferrag MA, Maglaras L, Moschoyiannis S, Janicke H (2020) Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. J Inf Secur Appl 50(102):419","journal-title":"J Inf Secur Appl"},{"issue":"1","key":"682_CR10","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/COMST.2022.3205643","volume":"25","author":"MU Hassan","year":"2022","unstructured":"Hassan MU, Rehmani MH, Chen J (2022) Anomaly detection in blockchain networks: a comprehensive survey. IEEE Commun Surv Tutor 25(1):289\u2013318","journal-title":"IEEE Commun Surv Tutor"},{"issue":"8","key":"682_CR11","doi-asserted-by":"publisher","first-page":"5278","DOI":"10.1109\/TWC.2022.3233035","volume":"22","author":"H Xiao","year":"2023","unstructured":"Xiao H, Cai L, Feng J, Pei Q, Shi W (2023) Resource optimization of MAB-based reputation management for data trading in vehicular edge computing. IEEE Trans Wirel Commun 22(8):5278\u20135290","journal-title":"IEEE Trans Wirel Commun"},{"issue":"5","key":"682_CR12","doi-asserted-by":"publisher","first-page":"3220","DOI":"10.1109\/TCOMM.2022.3163439","volume":"70","author":"J Feng","year":"2022","unstructured":"Feng J, Zhang W, Pei Q, Wu J, Lin X (2022) Heterogeneous computation and resource allocation for wireless powered federated edge learning systems. IEEE Trans Commun 70(5):3220\u20133233","journal-title":"IEEE Trans Commun"},{"key":"682_CR13","doi-asserted-by":"crossref","unstructured":"Yu J, Alhilal A, Zhou T, Pan H, Tsang DH (2023) Attention-based qoe-aware digital twin empowered edge computing for immersive virtual reality. arXiv preprint arXiv:2305.08569","DOI":"10.1109\/TWC.2024.3380820"},{"issue":"4","key":"682_CR14","doi-asserted-by":"publisher","first-page":"3570","DOI":"10.1109\/TSG.2021.3054375","volume":"12","author":"A Ahmed","year":"2021","unstructured":"Ahmed A, Sajan KS, Srivastava A, Wu Y (2021) Anomaly detection, localization and classification using drifting synchrophasor data streams. IEEE Trans Smart Grid 12(4):3570\u20133580","journal-title":"IEEE Trans Smart Grid"},{"issue":"4","key":"682_CR15","doi-asserted-by":"publisher","first-page":"3135","DOI":"10.1109\/JIOT.2021.3097437","volume":"9","author":"Y Feng","year":"2022","unstructured":"Feng Y, Liu Z, Chen J, Lv H, Wang J, Yuan J (2022) Make the rocket intelligent at iot edge: Stepwise gan for anomaly detection of lre with multisource fusion. IEEE Internet Things J 9(4):3135\u20133149","journal-title":"IEEE Internet Things J"},{"key":"682_CR16","doi-asserted-by":"crossref","unstructured":"Chang YY, Li P, Sosic R, Afifi MH, Schweighauser M, Leskovec J (2021) F-fade: Frequency factorization for anomaly detection in edge streams. In Proceedings of the 14th ACM international conference on web search and data mining, p 589\u2013597","DOI":"10.1145\/3437963.3441806"},{"key":"682_CR17","doi-asserted-by":"crossref","unstructured":"Eswaran D, Faloutsos C, Guha S, Mishra N (2018) Spotlight: detecting anomalies in streaming graphs. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, p 1378\u20131386","DOI":"10.1145\/3219819.3220040"},{"issue":"3","key":"682_CR18","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1109\/TITS.2019.2908074","volume":"21","author":"W Wu","year":"2019","unstructured":"Wu W, Li R, Xie G, An J, Bai Y, Zhou J, Li K (2019) A survey of intrusion detection for in-vehicle networks. IEEE Trans Intell Transp Syst 21(3):919\u2013933","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"1","key":"682_CR19","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/computers10010013","volume":"10","author":"MU Togbe","year":"2021","unstructured":"Togbe MU, Chabchoub Y, Boly A, Barry M, Chiky R, Bahri M (2021) Anomalies detection using isolation in concept-drifting data streams. Computers 10(1):13","journal-title":"Computers"},{"key":"682_CR20","doi-asserted-by":"publisher","first-page":"2300","DOI":"10.1109\/TIFS.2021.3053371","volume":"16","author":"S Han","year":"2021","unstructured":"Han S, Wu Q, Zhang H, Qin B, Hu J, Shi X, Liu L, Yin X (2021) Log-based anomaly detection with robust feature extraction and online learning. IEEE Trans Inf Forensic Secur 16:2300\u20132311","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"682_CR21","doi-asserted-by":"publisher","first-page":"116510","DOI":"10.1016\/j.eswa.2022.116510","volume":"193","author":"M Jain","year":"2022","unstructured":"Jain M, Kaur G, Saxena V (2022) A k-means clustering and svm based hybrid concept drift detection technique for network anomaly detection. Expert Syst Appl 193:116510","journal-title":"Expert Syst Appl"},{"issue":"4","key":"682_CR22","doi-asserted-by":"publisher","first-page":"3369","DOI":"10.1109\/COMST.2018.2854724","volume":"20","author":"A Nisioti","year":"2018","unstructured":"Nisioti A, Mylonas A, Yoo PD, Katos V (2018) From intrusion detection to attacker attribution: a comprehensive survey of unsupervised methods. IEEE Commun Surv Tutor 20(4):3369\u20133388","journal-title":"IEEE Commun Surv Tutor"},{"key":"682_CR23","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"682_CR24","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jnca.2015.12.004","volume":"62","author":"SY Ji","year":"2016","unstructured":"Ji SY, Jeong BK, Choi S, Jeong DH (2016) A multi-level intrusion detection method for abnormal network behaviors. J Netw Comput Appl 62:9\u201317","journal-title":"J Netw Comput Appl"},{"key":"682_CR25","doi-asserted-by":"publisher","first-page":"3110","DOI":"10.1609\/aaai.v35i4.16420","volume":"35","author":"X Yan","year":"2021","unstructured":"Yan X, Zhang H, Xu X, Hu X, Heng PA (2021) Learning semantic context from normal samples for unsupervised anomaly detection. Proceedings of the AAAI Conference on Artificial Intelligence 35:3110\u20133118","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"15","key":"682_CR26","doi-asserted-by":"publisher","first-page":"17708","DOI":"10.1007\/s10489-022-03300-1","volume":"52","author":"A Farzad","year":"2022","unstructured":"Farzad A, Gulliver TA (2022) Log message anomaly detection with fuzzy C-means and MLP. Appl Intell 52(15):17708\u201317717","journal-title":"Appl Intell"},{"key":"682_CR27","doi-asserted-by":"crossref","unstructured":"Graves A, Graves A (2012) Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37\u201345","DOI":"10.1007\/978-3-642-24797-2_4"},{"issue":"8","key":"682_CR28","doi-asserted-by":"publisher","first-page":"3127","DOI":"10.1109\/TNNLS.2019.2935975","volume":"31","author":"T Ergen","year":"2019","unstructured":"Ergen T, Kozat SS (2019) Unsupervised anomaly detection with lstm neural networks. IEEE Trans Neural Netw Learn Syst 31(8):3127\u20133141","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"682_CR29","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.autcon.2017.11.002","volume":"86","author":"L Ding","year":"2018","unstructured":"Ding L, Fang W, Luo H, Love PE, Zhong B, Ouyang X (2018) A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory. Autom Constr 86:118\u2013124","journal-title":"Autom Constr"},{"key":"682_CR30","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.inffus.2020.08.011","volume":"65","author":"E Jove","year":"2021","unstructured":"Jove E, Casteleiro-Roca JL, Quinti\u00e1n H, M\u00e9ndez-P\u00e9rez JA, Calvo-Rolle JL (2021) A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections. Inf Fusion 65:50\u201357","journal-title":"Inf Fusion"},{"issue":"4","key":"682_CR31","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/MSP.2018.2826566","volume":"35","author":"N Vaswani","year":"2018","unstructured":"Vaswani N, Bouwmans T, Javed S, Narayanamurthy P (2018) Robust subspace learning: Robust pca, robust subspace tracking, and robust subspace recovery. IEEE Signal Proc Mag 35(4):32\u201355","journal-title":"IEEE Signal Proc Mag"},{"key":"682_CR32","doi-asserted-by":"publisher","first-page":"107751","DOI":"10.1016\/j.asoc.2021.107751","volume":"112","author":"M Thill","year":"2021","unstructured":"Thill M, Konen W, Wang H, B\u00e4ck T (2021) Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Appl Soft Comput 112:107751","journal-title":"Appl Soft Comput"},{"key":"682_CR33","doi-asserted-by":"publisher","first-page":"9428","DOI":"10.1609\/aaai.v33i01.33019428","volume":"33","author":"A Borghesi","year":"2019","unstructured":"Borghesi A, Bartolini A, Lombardi M, Milano M, Benini L (2019) Anomaly detection using autoencoders in high performance computing systems. Proceedings of the AAAI Conference on Artificial Intelligence 33:9428\u20139433","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"5","key":"682_CR34","doi-asserted-by":"publisher","first-page":"2978","DOI":"10.1109\/TNSE.2022.3163144","volume":"10","author":"H Gao","year":"2022","unstructured":"Gao H et al (2022) Tsmae: a novel anomaly detection approach for internet of things time series data using memory-augmented autoencoder. IEEE Trans Netw Sci Eng 10(5):2978\u20132990","journal-title":"IEEE Trans Netw Sci Eng"},{"issue":"4","key":"682_CR35","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1007\/s40815-020-00845-6","volume":"22","author":"HG Han","year":"2020","unstructured":"Han HG, Zhang HJ, Qiao JF (2020) Robust deep neural network using fuzzy denoising autoencoder. Int J Fuzzy Syst 22(4):1356\u20131375","journal-title":"Int J Fuzzy Syst"},{"key":"682_CR36","doi-asserted-by":"crossref","unstructured":"Geiger A, Liu D, Alnegheimish S, Cuesta-Infante A, Veeramachaneni K (2020) Tadgan: Time series anomaly detection using generative adversarial networks. In: 2020 IEEE International Conference on Big Data (Big Data), IEEE, pp 33\u201343","DOI":"10.1109\/BigData50022.2020.9378139"},{"key":"682_CR37","doi-asserted-by":"crossref","unstructured":"Li D, Chen D, Jin B, Shi L, Goh J, Ng SK (2019) Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks. In: International conference on artificial neural networks. Springer, pp 703\u2013716","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"682_CR38","doi-asserted-by":"crossref","unstructured":"Bashar MA, Nayak R (2020) Tanogan: Time series anomaly detection with generative adversarial networks. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp 1778\u20131785","DOI":"10.1109\/SSCI47803.2020.9308512"},{"issue":"4","key":"682_CR39","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1007\/s11277-016-3433-3","volume":"90","author":"M Wazid","year":"2016","unstructured":"Wazid M, Das AK (2016) An efficient hybrid anomaly detection scheme using k-means clustering for wireless sensor networks. Wirel Pers Commun 90(4):1971\u20132000","journal-title":"Wirel Pers Commun"},{"key":"682_CR40","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf B, Williamson RC, Smola A, Shawe-Taylor J, Platt J (1999) Support vector method for novelty detection.\u00a0Adv Neural Inf Process Syst 12","DOI":"10.1162\/089976600300015565"},{"key":"682_CR41","unstructured":"Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34), p 226\u2013231"},{"key":"682_CR42","doi-asserted-by":"crossref","unstructured":"Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p 93\u2013104","DOI":"10.1145\/342009.335388"},{"key":"682_CR43","doi-asserted-by":"crossref","unstructured":"Liu FT, Ting KM, Zhou ZH (2008) Isolation forest. In: 2008 eighth ieee international conference on data mining. IEEE, pp 413\u2013422","DOI":"10.1109\/ICDM.2008.17"},{"issue":"1","key":"682_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2133360.2133363","volume":"6","author":"FT Liu","year":"2012","unstructured":"Liu FT, Ting KM, Zhou ZH (2012) Isolation-based anomaly detection. ACM Trans Knowl Discov Data 6(1):1\u201339","journal-title":"ACM Trans Knowl Discov Data"},{"key":"682_CR45","doi-asserted-by":"crossref","unstructured":"Reynolds DA (2009) Gaussian mixture models. Encyclopedia of biometrics 741, p 659\u2013663","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"682_CR46","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. PMLR, pp 2712\u20132721"},{"key":"682_CR47","doi-asserted-by":"publisher","first-page":"107015","DOI":"10.1016\/j.cie.2020.107015","volume":"152","author":"C Xu","year":"2021","unstructured":"Xu C, Wang J, Zhang J, Li X (2021) Anomaly detection of power consumption in yarn spinning using transfer learning. Comput Ind Eng 152:107015","journal-title":"Comput Ind Eng"},{"issue":"106","key":"682_CR48","first-page":"816","volume":"216","author":"G Michau","year":"2021","unstructured":"Michau G, Fink O (2021) Unsupervised transfer learning for anomaly detection: application to complementary operating condition transfer. Knowl-Based Syst 216(106):816","journal-title":"Knowl-Based Syst"},{"key":"682_CR49","unstructured":"Ahmad S, Lavin A, Purdy S, Agha Z (2017a) The numenta anomaly benchmark [white paper]. https:\/\/github.com\/numenta\/NAB\/wiki. Accessed\u00a010\u00a0Oct\u00a02022"},{"key":"682_CR50","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.neucom.2017.04.070","volume":"262","author":"S Ahmad","year":"2017","unstructured":"Ahmad S, Lavin A, Purdy S, Agha Z (2017) Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262:134\u2013147","journal-title":"Neurocomputing"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-024-00682-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-024-00682-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-024-00682-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T14:28:40Z","timestamp":1720189720000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-024-00682-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,5]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["682"],"URL":"https:\/\/doi.org\/10.1186\/s13677-024-00682-0","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,5]]},"assertion":[{"value":"7 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This research does not consider any of human and\/or animal studies which may cause any ethical concern, thus the research does not need to recieve ethical approvals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"120"}}