Enhancing network slice security with Deep Reinforcement Learning and Moving Target Defense strategies

Enhancing network slice security with Deep Reinforcement Learning and Moving Target Defense strategies

Abstract Network slicing is revolutionizing how networks are built and managed by enabling the flexible and efficient allocation of resources to meet diverse application requirements. Yet this flexibility introduces significant security challenges that must be addressed to maintain system integrity...

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Bibliographic Details
Main Authors: Andreas Andreou, Constandinos X. Mavromoustakis, Evangelos Markakis, Athina Bourdena, George Mastorakis
Format: Article
Language:English
Published: Springer 2025-06-01
Series:Discover Internet of Things
Subjects:
Network slicing
Deep Reinforcement Learning (DRL)
Moving Target Defense (MTD)
Markov Decision Process (MDP)
Next-generation networks
Online Access:https://doi.org/10.1007/s43926-025-00161-1
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https://doi.org/10.1007/s43926-025-00161-1

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