Adaptive Intrusion Mitigation in Software-Defined Vehicles Using Deep Reinforcement Learning

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dc.catalogadorjca
dc.contributor.authorKurunathan, Harrison
dc.contributor.authorIsmail Ali, Hazem
dc.contributor.authorJavanmardi, Gowhar
dc.contributor.authorEldefrawy, Mohamed
dc.contributor.authorGutiérrez Gaitán, Miguel José
dc.contributor.authorRobles, Ramiro
dc.contributor.authorYomsi, Patrick
dc.contributor.authorTovar, Eduardo
dc.date.accessioned2025-05-12T14:42:35Z
dc.date.available2025-05-12T14:42:35Z
dc.date.issued2025
dc.description.abstractSoftware-defined vehicles (SDVs) leverage vehicle-to-everything (V2X) communication to enable advanced connectivity and autonomous driving capabilities. However, this increased interconnectivity also exposes them to cyber threats such as spoofing, denial-of-service attacks, and data manipulation, making intrusion detection systems (IDS) essential for ensuring SDV security and reliability. In this work, we propose a novel intrusion mitigation approach that integrates Advantage Actor-Critic (A2C) reinforcement learning with a Long Short-Term Memory (LSTM) network to detect anomalies and intrusions in V2X communications. The LSTM component captures temporal dependencies in V2X data, enhancing the model's ability to identify emerging attack patterns, while the A2C framework dynamically adjusts defensive actions, including flagging, blocking or monitoring traffic, based on evolving threat levels. Experimental results demonstrate the model's effectiveness, achieving high detection accuracy and sensitivity. Additionally, we analyze how the system adapts over time, becoming more confident in its decision-making and optimizing security enforcement. This work enhances SDV cybersecurity by introducing a learning-based adaptive intrusion response system aiming at mitigating threats in highly dynamic vehicular networks.
dc.fechaingreso.objetodigital2025-05-12
dc.format.extent6 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1145/3722567.3727848
dc.identifier.isbn979-8400716119
dc.identifier.urihttps://doi.org/10.1145/3722567.3727848
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/104130
dc.information.autorucEscuela de Ingeniería; Gutiérrez Gaitán, Miguel José; 0000-0002-3307-8731; 1352133
dc.issue.numero4
dc.language.isoen
dc.nota.accesocontenido completo
dc.relation.ispartofRAGE '25: Proceedings of the 4th International Workshop on Real-time and IntelliGent Edge computing
dc.rightsacceso abierto
dc.rights.licenseAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectSoftware-defined vehicles
dc.subjectIntrusion mitigation
dc.subjectDeep reinforcement learning
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleAdaptive Intrusion Mitigation in Software-Defined Vehicles Using Deep Reinforcement Learning
dc.typecomunicación de congreso
sipa.codpersvinculados1352133
sipa.trazabilidadORCID;2025-05-07
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