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Over the years, machine learning models have progressed to be integrated into many scenarios to detect anomalies accurately. This paper proposes a novel approach named cloud-based anomaly detection (<jats:italic>CAD<\/jats:italic>) to detect cloud-based anomalies. <jats:italic>CAD<\/jats:italic> consist of two key blocks: ensemble machine learning (EML) model for binary anomaly classification and convolutional neural network long short-term memory (<jats:italic>CNN-LSTM<\/jats:italic>) for multiclass anomaly categorization. <jats:italic>CAD<\/jats:italic> is evaluated on a complex UNSW dataset to analyze the performance of binary anomaly detection and categorization of multiclass anomalies. Furthermore, the comparison of <jats:italic>CAD<\/jats:italic> with other machine learning conventional models and state-of-the-art studies have been presented. Experimental analysis shows that <jats:italic>CAD<\/jats:italic> outperforms other studies by achieving the highest accuracy of 97.06% for binary anomaly detection and 99.91% for multiclass anomaly detection.<\/jats:p>","DOI":"10.1186\/s13677-022-00329-y","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T11:07:34Z","timestamp":1667473654000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Cloud-based multiclass anomaly detection and categorization using ensemble learning"],"prefix":"10.1186","volume":"11","author":[{"given":"Faisal","family":"Shahzad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul","family":"Mannan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul Rehman","family":"Javed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad S.","family":"Almadhor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thar","family":"Baker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhiya","family":"Al-Jumeily OBE","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"issue":"2","key":"329_CR1","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1007\/s40815-021-01104-y","volume":"24","author":"A Mohiyuddin","year":"2022","unstructured":"Mohiyuddin A, Javed AR, Chakraborty C, Rizwan M, Shabbir M, Nebhen J (2022) Secure cloud storage for medical iot data using adaptive neuro-fuzzy inference system. 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