Fast Learning Enabled by In-Network Drift Detection

Author:

Xavier Bruno Missi1ORCID,Martinello Magnos2ORCID,Trois Celio3ORCID,Alenca Brenno M.4ORCID,Rios Ricardo A.4ORCID

Affiliation:

1. Federal Institute of Espírito Santo/Brazil, Brazil

2. Federal University of Espírito Santo/Brazil, Brazil

3. Federal University of Santa Maria/Brazil, Brazil

4. Federal University of Bahia/Brazil, Brazil

Funder

CNPq, CAPES, FAPESP/MCTI/CGI.br

FAPES

Publisher

ACM

Reference24 articles.

1. Emad Alsuwat, Suhare Solaiman, and Hatim Alsuwat. 2023. Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing.Computers, Materials & Continua 75, 2 (2023).

2. INSOMNIA

3. Fabrício Ceschin, Marcus Botacin, Albert Bifet, Bernhard Pfahringer, Luiz S Oliveira, Heitor Murilo Gomes, and André Grégio. 2020. Machine learning (in) security: A stream of problems. Digital Threats: Research and Practice (2020).

4. Joao Gama 2004. Learning with drift detection. In Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17. Springer, 286–295.

5. João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A survey on concept drift adaptation. ACM computing surveys (2014).

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