The Choice of Training Data and the Generalizability of Machine Learning Models for Network Intrusion Detection Systems

The Choice of Training Data and the Generalizability of Machine Learning Models for Network Intrusion Detection Systems

Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies the generalizability o...

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Bibliographic Details
Main Authors: Marcin Iwanowski, Dominik Olszewski, Waldemar Graniszewski, Jacek Krupski, Franciszek Pelc
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
intrusion detection system
internet traffic classification
machine learning
Online Access:https://www.mdpi.com/2076-3417/15/15/8466
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