BIBLIOS

Document type
Conference papers

Document subtype
Full paper

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

Participants in the publication
Eduardo K. Viegas (Author)
Altair O. Santin (Author)
Vinicius V. Cogo (Author)
Dep. Informática
LASIGE
Vilmar Abreu (Author)

Summary
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.


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

Publication Identifiers
ISBN - 9781728150895

Address
Dublin, Ireland

Publisher
IEEE

Document Identifiers
DOI - https://doi.org/10.1109/icc40277.2020.9148916
URL - http://dx.doi.org/10.1109/icc40277.2020.9148916

Rankings
CORE B (2018) -