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Cited by 6Network Security and Intrusion DetectionAdvanced Malware Detection TechniquesNetwork Packet Processing and Optimization

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Current and Future RL’s Contribution to Emerging Network Security

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Feltus, Christophe·2020·Procedia Computer Science
DOI 10.1016/j.procs.2020.10.071
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Abstract

Reinforcement learning is a machine-learning paradigm, which learns the best actions an agent needs to perform to maximize its rewards in a particular environment. Research into RL has been proven to have made a real contribution to the protection of emerging network systems against malware. In this paper, a systematic review of this research was performed in regard to various attacks and an analysis of the trends and future fields of interest for the RL-based research in network security was completed.

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References 34 · Cited by 6

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Metadata

Authors
Feltus, C.
Affiliations
Luxembourg Institute of Science and Technology (LU)
Corresponding author
Feltus, Christophe
ORCID
Feltus, Christophe
Venue
Procedia Computer Science
Publisher
Elsevier BV
Published
2020
Volume
177
Pages
516-521
ISSN
1877-0509 (ISSN-L)
DOI
10.1016/j.procs.2020.10.071
OpenAlex
W3098999489
Cited by
6
References
34
Citations by year
2024: 1 · 2022: 3 · 2021: 2
FWCI
1.05
Type
conference paper
Language
en
Open access
Licence
Locations
Procedia Computer Science (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Software › Advanced Malware Detection Techniques
  • Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
Keywords
reinforcement learning, network security, malware detection, cyberattacks, research trends

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CoChat. (2026). Citation check: Current and Future RL’s Contribution to Emerging Network Security. https://app.cochat.ai/papers/10.1016/j.procs.2020.10.071
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Feltus, C. (2020). Current and Future RL’s Contribution to Emerging Network Security. Procedia Computer Science. https://doi.org/10.1016/j.procs.2020.10.071