BIBLIOS

Tipo
Artigos em Conferência

Tipo de Documento
Artigo Completo

Título
A Reliable Semi-Supervised Intrusion Detection Model: One Year of Network Traffic Anomalies

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

Resumo
Despite the promising results of machine learning for network-based intrusion detection, current techniques are not widely deployed in real-world environments. In general, proposed detection models quickly become obsolete, thus, generating unreliable classifications over time. In this paper, we propose a new reliable model for semi-supervised intrusion detection that uses a verification technique to provide reliable classifications over time, even in the absence of model updates. Additionally, we cope with this verification technique with semi-supervised learning to autonomously update the underlying machine learning models without human assistance. Our experiments consider a full year of real network traffic and demonstrate that our solution maintains the accuracy rate over time without model updates while rejecting only 10.6% of instances on average. Moreover, when autonomous (non-human-assisted) model updates are performed, the average rejection rate drops to just 3.2% without affecting the accuracy of our solution.


Evento
ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

Identificadores da Publicação
ISBN - 9781728150895

Local
Dublin, Ireland

Editora
IEEE

Identificadores do Documento
DOI - https://doi.org/10.1109/icc40277.2020.9148916
URL - http://dx.doi.org/10.1109/icc40277.2020.9148916

Identificadores de Qualidade
CORE B (2018) -