Resumo

LOPEZ-AVILA, Leyanis; ACOSTA-MENDOZA, Niusvel  e  GAGO-ALONSO, Andrés. Anomaly Detection based on Deep Learning: Review. Rev cuba cienc informat [online]. 2019, vol.13, n.3, pp. 107-123. ISSN 2227-1899.

Anomaly detection is a Data Mining technique that allows the recognition of new patterns with unusual behavior, which can be translated as non-valid actions or anomalies over the data. Anomaly detection has allowed the identification and prevention of malicious activities such as fraud and intrusions, among others. The use of traditional techniques of anomaly detection has reported very good results. However, in the last years, more relevant results have been reported through the use of deep learning techniques. The aim of this report is to give a revision of the principal and most recent state-of-the-art methods for anomaly (fraud and intrusions) detection based on the deep learning technique, which we categorized according to the kind of the used deep neural network.

Palavras-chave : Deep learning-based anomaly detection; fraud detection; intrusion detection; deep learning.

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