Apr 2, 2019 · We propose a general active learning framework and experiment with different choices of learners and sampling strategies.
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Active learning is a family of approaches selecting samples for labeling to build classifier with maximum prediction accuracy. So it is able to improve the ...
We propose an effective active learning strategy to query low-confidence observations and to expand the data basis with minimal labeling effort. Our empirical ...
Abstract. Network operators are generally aware of common attack vectors that they defend against. For most networks the vast majority of traffic is ...
Additionally, we propose an active learning strategy to automatically filter candidates for labeling. In an empirical study on network intrusion detection data, ...
In this paper, we discuss the usage of active learning in online configuration to reduce the labeling cost but maintaining the classification performance.
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ABSTRACT. Anomaly detection for network intrusion detection is usu- ally considered an unsupervised task. Prominent techniques,.
The current paper fills this research gap and studies the use of active learning techniques for NIDS alert classification.
Apr 10, 2024 · In this study, we propose a signature-based network intrusion detection system that incorporates active learning to efficiently learn new types ...
Mar 11, 2020 · The task of the machine learning algorithm is, given the information of a network flow, classify the network flow in one of two classes: benign ...