Adaptive Anomaly Detection Strategy Based on Reinforcement Learning

Xu, Youchang; Chen, Ningjiang; Zhang, Hanlin; Liang, Birui

doi:10.1007/978-981-13-2206-8_40

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…References Time series analysis [162], [161], [76], [198], [83], [71], [210], [70] Bayesian learning [118], [85], [14], [103], [159], [50], [35], [128], [105], [157] Principal component analysis [195], [5], [18], [113], [108] Regression analysis [205], [54], [25], [89], [156] Logistic regression [131], [115], [26], [207] Hidden Markov model [79], [78], [11], [170] Markov chain [167], [52], [41] Gaussian mixture models [106], [13] Statistical tests [173], [114] Restricted Boltzmann machine [110], [ [201], [200], [63], Wavelet transform…”

Section: Methodsmentioning

confidence: 99%

A Systematic Review on Anomaly Detection for Cloud Computing Environments

Hagemann

Katsarou

2020

2020 3rd Artificial Intelligence and Cloud Computing Conference

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The detection of anomalies in data is a far-reaching field of research which also applies to the field of cloud computing in several different ways: from the detection of various types of intrusions to the detection of hardware failures, many publications address how far anomaly detection methods are able to meet the specific requirements of a cloud-based network. Since there is still no comprehensive overview of this constantly growing field of research, this literature review provides a systematic evaluation of 215 publications that can be considered as representative for the last ten years of this scientific development. Our analysis identifies three main methodological areas (machine learning, deep learning, statistical approaches) and summarizes how exactly the corresponding models are applied for the detection of anomalies. Furthermore, we clarify which concrete application areas are typically addressed by anomaly detection in the context of cloud computing environments and which related public datasets are often used for evaluations. Finally, we discuss the implications of the literature review and provide directions for future research.

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