A lightweight unsupervised learning architecture to enhance user behavior anomaly detection (2022)
- Authors:
- Autor USP: MENEGUETTE, RODOLFO IPOLITO - ICMC
- Unidade: ICMC
- DOI: 10.1109/LATINCOM56090.2022.10000477
- Subjects: ANÁLISE DO COMPORTAMENTO; ANÁLISE DE DADOS; REDES NEURAIS
- Keywords: User Behavior Anomaly Detection; Autoencoders; Wide and Deep Neural Networks; Long Short-Term Memory Autoencoder; temporal convolution
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2022
- Source:
- Título: Proceedings
- Conference titles: Latin-American Conference on Communications - LATINCOM
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
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: 26 dez. 2025. -
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/LATINCOM56090.2022.10000477 -
NLM
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 2025 dez. 26 ] Available from: https://doi.org/10.1109/LATINCOM56090.2022.10000477 -
Vancouver
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 2025 dez. 26 ] Available from: https://doi.org/10.1109/LATINCOM56090.2022.10000477 - F-NIDS: sistema de detecção de intrusão descentralizado com base em aprendizado federado
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Informações sobre o DOI: 10.1109/LATINCOM56090.2022.10000477 (Fonte: oaDOI API)
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