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Secure industrial IoT gateway

Abstract

The Secure Industrial IoT Gateway project aims to push the state-of-the-art in secure communication for the Industrial Internet of Things (IIoT), focusing on the gateways that connect equipments and machines to the Internet and the Cloud. The project addresses the issue from two perspectives: making gateways more robust against attacks and detecting violations in case they occur. For the first aspect, the project will explore tools to validate the software at the core of gateways, mechanisms to isolate such software in Trusted Execution Environments, and techniques to promote redundancy and diversity in the sense of fault tolerance. For the second aspect, the project will investigate secure protocols to check the integrity of the data generated by IIoT devices on their way to the Cloud, and also non-intrusive monitoring systems to detect anomalous behavior of gateways. The project will be validated through a proof-of-concept implementation (for performance and energy-efficiency assessment) and simulations (for scalability assessment). Security will be evaluated in terms of robustness, hit rate, and false-positive rate. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
FROEHLICH, ANTONIO AUGUSTO; HORSTMANN, LEONARDO PASSIG; HOFFMANN, JOSE LUIS CONRADI. A Secure IIoT Gateway Architecture based on Trusted Execution Environments. Journal of Network and Systems Management, v. 31, n. 2, p. 30-pg., . (21/02384-7, 20/05142-1, 21/02385-3)
HORSTMANN, LEONARDO PASSIG; FROHLICH, ANTONIO AUGUSTO; IEEE. Intrusion Detection in Multicore Embedded Systems based on Artificial Immune Systems. 2022 IEEE 27TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), v. N/A, p. 8-pg., . (20/05142-1, 21/02385-3)
DE LUCENA, MATEUS MARTINEZ; FROHLICH, ANTONIO AUGUSTO; IEEE. Modeling Misbehavior Detection Timeliness in VANETs. 2022 IEEE 27TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), v. N/A, p. 8-pg., . (20/05142-1)