Improving Generalization of ML-Based IDS With Lifecycle-Based Dataset, Auto-Learning Features, and Deep Learning

Improving Generalization of ML-Based IDS With Lifecycle-Based Dataset, Auto-Learning Features, and Deep Learning

During the past 10 years, researchers have extensively explored the use of machine learning (ML) in enhancing network intrusion detection systems (IDS). While many studies focused on improving accuracy of ML-based IDS, true effectiveness lies in robust generalization: the ability to classify unseen...

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Bibliographic Details
Main Authors: Didik Sudyana, Ying-Dar Lin, Miel Verkerken, Ren-Hung Hwang, Yuan-Cheng Lai, Laurens D'Hooge, Tim Wauters, Bruno Volckaert, Filip De Turck
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Machine Learning in Communications and Networking
Subjects:
Intrusion detection
ML-based IDS
model generalization
lifecycle-based dataset
auto-learning features
Online Access:https://ieeexplore.ieee.org/document/10531223/
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