BIBLIOS

Tipo
Artigos em Conferência

Tipo de Documento
Artigo Completo

Título
A Long-Lasting Reinforcement Learning Intrusion Detection Model

Participantes na publicação
Roger Robson dos Santos (Author)
Eduardo Kugler Viegas (Author)
Altair Santin (Author)
Vinicius Vielmo Cogo (Author)
Dep. Informática
LASIGE

Resumo
Several works have proposed highly accurate network-based intrusion detection schemes through machine learning techniques. However, they are unable to address changes in network traffic behavior over time. Authors often assume periodic model updates, but without taking into account the challenges they entail. This paper proposes a long-lasting reinforcement learning model for intrusion detection that withstands long periods without model updates. Our proposal builds machine learning models through reinforcement learning to keep their accuracy for longer periods. Then, we cope it with a verification technique to ensure that only reliable classifications are accepted over time. Experiments performed using a dataset spanning a year of real network traffic, composed of 10 TB of data, show that the technique we propose remains reliable for ten months without model updates. Additionally, our proposal increases its accuracy when coped with the verification technique.


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_121
URL - http://dx.doi.org/10.1007/978-3-030-44041-1_121

Identificadores de Qualidade
CORE B (2020) -