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

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.

Virtual Private Network—Firewall Integration for Wireless Local Area Network Improvement against Jammers

“…The outcomes demonstrated that these routing algorithms significantly impacted network performance. A machinelearning method to defend against jamming assaults in underwater networks was presented by Mertens et al [17] This applies to security applications where sensor devices are placed in high-risk areas. The proposed approach is efficient in reducing unnecessary energy use, according to extensive simulation and performance research.…”