This paper proposes a new stream learning intrusion detection aiming for feasible model updates, implemented in three phases.

Abstract—Network-based intrusion detection is a widely ex- plored topic in the literature. Yet, despite the promising reported.

Experiments performed on a dataset containing evolving network traffic behavior have shown the proposal feasibility, reaching up to 96% of accuracy while ...

Oct 11, 2022 · This paper proposes a new stream learning intrusion detection aiming for feasible model updates, implemented in three phases. First, intrusion ...

Nov 21, 2024 · Concept drift adaptation usually applies a drift detection algorithm combined with a machine learning model. When drift is detected, the model ...

First, intrusion detection is performed through a stream learning classifier, enabling incremental model updates to be performed. Second, new network traffic ...

Oct 11, 2021 · To combat concept drift, we describe a methodology to update a deep neural network architecture over a network traffic data stream. It ...

It integrates a concept drift detection mechanism to discover incoming traffic that deviates from the past and triggers the fine-tuning of the deep neural ...

A Stream Learning Intrusion Detection System for Concept Drifting Network Traffic ... concept drift-driven security approach for network introspection.

We propose a concept drift detector based on conditional variational autoencoder ( CVAE ) under the realistic assumption of limited access to the labeled ...