AI’S Contribution to Ubiquitous Systems and Pervasive Networks Security – Reinforcement Learning vs Recurrent Networks [* Cross-Reference *]

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Christophe Feltus, 2026, "AI’S Contribution to Ubiquitous Systems and Pervasive Networks Security – Reinforcement Learning vs Recurrent Networks [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.0MJAPP, LIST Open Repository, V1
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Description
Reinforcement learning and recurrent networks are two emerging machine-learning paradigms. The first learns the best actions an agent needs to perform to maximize its rewards in a particular environment and the second has the specificity to use an internal state to remember previous analysis results and consider them for the current one. Research into RL and recurrent network has been proven to have made a real contribution to the protection of ubiquitous systems and pervasive networks against intrusions and malwares. 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 and recurrent network-based research in network security was complete. (2021-03-01)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5383/juspn.15.02.001 for the original and latest version of the publication*** (2026-07-01)
Subject Computer and Information Science; Engineering; Physics
Keyword Reinforcement learning, Computer science, Ubiquitous computing, Artificial intelligence, Network security, Computer security, State (computer science), Machine learning, Human–computer interaction
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