Document type
Conference papers
Document subtype
Full paper
Title
Facing the Unknown: A Stream Learning Intrusion Detection System for Reliable Model Updates
Participants in the publication
Eduardo K. Viegas (Author)
Altair O. Santin (Author)
Vinicius V. Cogo (Author)
Dep. Informática
LASIGE
Vilmar Abreu (Author)
Summary
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.
Date of Publication
2020-04-15
Event
Advanced Information Networking and Applications,Advances in Intelligent Systems and Computing
Publication Identifiers
ISSN - 2194-5357,2194-5365
ISBN - 9783030440404,9783030440411
Publisher
Springer International Publishing
Number of pages
11
Starting page
898
Last page
909
Document Identifiers
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
Rankings
CORE B (2020) -