A lightweight unsupervised learning architecture to enhance user behavior anomaly detection (2022)
Molina, André L. B; Gonçalves, Vinicius Pereira 
; Sousa Júnior, Rafael Timóteo de ; Pividal, Marcel; Meneguette, Rodolfo Ipolito ; Rocha Filho, Geraldo Pereira
Fonte: Proceedings. Nome do evento: Latin-American Conference on Communications - LATINCOM. Unidade: ICMC
Assuntos: ANÁLISE DO COMPORTAMENTO, ANÁLISE DE DADOS, REDES NEURAIS
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ABNT
MOLINA, André L. B et al. A lightweight unsupervised learning architecture to enhance user behavior anomaly detection. 2022, Anais.. Piscataway: IEEE, 2022. Disponível em: https://doi.org/10.1109/LATINCOM56090.2022.10000477. Acesso em: 14 nov. 2024.APA
Molina, A. L. B., Gonçalves, V. P., Sousa Júnior, R. T. de, Pividal, M., Meneguette, R. I., & Rocha Filho, G. P. (2022). A lightweight unsupervised learning architecture to enhance user behavior anomaly detection. In Proceedings. Piscataway: IEEE. doi:10.1109/LATINCOM56090.2022.10000477NLM
Molina ALB, Gonçalves VP, Sousa Júnior RT de, Pividal M, Meneguette RI, Rocha Filho GP. A lightweight unsupervised learning architecture to enhance user behavior anomaly detection [Internet]. Proceedings. 2022 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1109/LATINCOM56090.2022.10000477Vancouver
Molina ALB, Gonçalves VP, Sousa Júnior RT de, Pividal M, Meneguette RI, Rocha Filho GP. A lightweight unsupervised learning architecture to enhance user behavior anomaly detection [Internet]. Proceedings. 2022 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1109/LATINCOM56090.2022.10000477
