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

5Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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.

Cite

CITATION STYLE

APA

Viegas, E. K., Santin, A. O., Cogo, V. V., & Abreu, V. (2020). Facing the Unknown: A Stream Learning Intrusion Detection System for Reliable Model Updates. In Advances in Intelligent Systems and Computing (Vol. 1151 AISC, pp. 898–909). Springer. https://doi.org/10.1007/978-3-030-44041-1_78

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free