BIBLIOS

Tipo
Artigos em Conferência

Tipo de Documento
Artigo Completo

Título
Facing the Unknown: A Stream Learning Intrusion Detection System for Reliable Model Updates

Participantes na publicação
Eduardo K. Viegas (Author)
Altair O. Santin (Author)
Vinicius V. Cogo (Author)
Dep. Informática
LASIGE
Vilmar Abreu (Author)

Resumo
Current machine learning approaches for network-based intrusion detection do not cope with new network traffic behavior, which requires periodic computationally and time-consuming model updates. In light of this limitation, this paper proposes a novel stream learning intrusion detection model that maintains system accuracy, even in the presence of unknown traffic behavior. It also eases the model update process by incrementally incorporating new knowledge into the machine learning model. Experiments performed using a recent realistic dataset of network behaviors have shown that the proposed technique detects potentially unreliable classifications. Moreover, the proposed model can incorporate the new network traffic behavior from model updates to improve the system accuracy while maintaining its reliability.


Evento
Advanced Information Networking and Applications,Advances in Intelligent Systems and Computing

Identificadores da Publicação
ISSN - 2194-5357,2194-5365
ISBN - 9783030440404,9783030440411

Editora
Springer International Publishing


Identificadores do Documento
DOI - https://doi.org/10.1007/978-3-030-44041-1_78
URL - http://dx.doi.org/10.1007/978-3-030-44041-1_78

Identificadores de Qualidade
CORE B (2020) -