In this paper, we present a machine-learning technique to counteract jamming attacks in underwater networks. Indeed, this is relevant in security applications where sensor devices are located in critical regions, for example, in the case of national border surveillance or for identifying any unauthorized intrusion. To this aim, a multi-hop routing protocol that relies on the exploitation of a Q-learning methodology is presented with a focus on increasing reliability in data communication and network lifetime. Performance results assess the effectiveness of the proposed solution as compared to other efficient state-of-the-art approaches.

Network intelligence vs. jamming in underwater networks: how learning can cope with misbehavior

Panebianco A.
Membro del Collaboration Group
;
Galluccio L.
Supervision
2023-01-01

Abstract

In this paper, we present a machine-learning technique to counteract jamming attacks in underwater networks. Indeed, this is relevant in security applications where sensor devices are located in critical regions, for example, in the case of national border surveillance or for identifying any unauthorized intrusion. To this aim, a multi-hop routing protocol that relies on the exploitation of a Q-learning methodology is presented with a focus on increasing reliability in data communication and network lifetime. Performance results assess the effectiveness of the proposed solution as compared to other efficient state-of-the-art approaches.
2023
underwater networks
jamming attacks
Q-learning
routing
Markov model
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/575090
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