ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 2045-2322

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model ; volume:12 ; number:1 ; day:13 ; month:9 ; year:2022 ; pages:1-18 ; date:12.2022
Scientific reports ; 12, Heft 1 (13.9.2022), 1-18, 12.2022

Creator: Ren, Kezhou
Zeng, Yifan
Cao, Zhiqin
Zhang, Yingchao

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-022-19366-3

URN: urn:nbn:de:101:1-2022113021194605978063

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.04.2024, 9:27 AM CEST

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Ren, Kezhou
Zeng, Yifan
Cao, Zhiqin
Zhang, Yingchao
SpringerLink (Online service)

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ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model

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Other Objects (12)

MAFSIDS: a reinforcement learning-based intrusion detection model for multi-agent feature selection networks

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Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning

Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks

Exploring feature selection scenarios for deep learning-based side-channel snalysis

Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning

An improved equilibrium optimization algorithm for feature selection problem in network intrusion detection

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GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning

Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks

Exploring feature selection scenarios for deep learning-based side-channel snalysis

Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning

An improved equilibrium optimization algorithm for feature selection problem in network intrusion detection

A novel ensemble learning-based model for network intrusion detection

Reinforcement learning-based estimation for spatio-temporal systems

MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs

A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks

MAFSIDS: a reinforcement learning-based intrusion detection model for multi-agent feature selection networks

Heterogeneous Ensemble Feature Selection for Network Intrusion Detection System

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning

Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks

Exploring feature selection scenarios for deep learning-based side-channel snalysis

Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning

An improved equilibrium optimization algorithm for feature selection problem in network intrusion detection

A novel ensemble learning-based model for network intrusion detection

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A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks

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