Anomaly detection in cloud services using unsupervised learning techniques
Keywords:
Unsupervised learning, anomaly detection, cloud computingAbstract
Early anomaly detection in cloud services has become a critical challenge due to the increasing complexity of technological infrastructures. This study aims to analyze and evaluate the effectiveness of unsupervised learning techniques for anomaly detection in cloud computing environments, providing a comprehensive analytical framework to facilitate the selection and implementation of appropriate algorithms according to specific contexts. The research employs a qualitative methodology with a descriptive bibliographic approach, based on the systematic analysis of specialized scientific literature published between 2018 and 2024, including a detailed examination of case studies that demonstrate the practical application of these techniques in real-world environments. The findings reveal that the effectiveness of algorithms varies significantly according to the application context, where techniques such as Isolation Forest excel in handling high-dimensional data, achieving accuracy levels of 99% in fraud detection when implemented in conjunction with distributed processing tools. Likewise, there is evidence of a trend towards the implementation of hybrid solutions that combine multiple techniques, as demonstrated by the SAGE system, which integrates Causal Bayesian Networks with Variational Autoencoders for the effective identification of performance problems in microservices systems. Finally, it is established that effective anomaly detection in cloud services requires an adaptive approach that combines multiple unsupervised learning techniques, the selection of which should be based on the specific characteristics of the environment and the particular requirements of each implementation. This research not only contributes to methodological advancement in the field but also provides practical guidelines for optimizing the security and efficiency of cloud computing infrastructures.
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