Providing Resilient & secure networks [Operating on Trusted Equipment] to CriTical infrastructures (PROTECT)
Forskningsprojekt , 2021 – 2024

The primary objective of the AI-NET research program is to Accelerate digital transformation in Europe by Intelligent NETwork automation at each network segment, i.e., edge, metro, core, and data centers. To this end, the program will provide enablers and solutions for high-performance services deployed and operated over the access/edge, metro, and core infrastructures. According to the AI-NET vision, high-level languages and tools will be exposed to a service developer, to simplify the development of new services and enable flexible specification of performance requirements. Once developed, service deployment and setup are managed by an intent-based software-defined network management and control, which transfers service requirements to detailed operational procedures and end-to-end network configurations, for the fulfillment of the service requirements over its lifetime.
The primary focus of the AI-NET-PROTECT sub-project is to provide automated, resilient, and secure networks (operated on trusted equipment) for critical infrastructures and enterprises. AI-NET-PROTECT will ensure the protection of critical data, adherence to network performance requirements (e.g., latency, throughput, availability), and the security of the infrastructure itself (i.e., against tampering and attacks). To achieve these objectives, the project will develop a scalable network and node architecture composed of a mix of open and purpose-built hardware and software, including Whiteboxes. Network telemetry and intent-based software-defined network management and control will provide zero-touch provisioning and support artificial-intelligence-based automation of end-to-end services. Strong security, based on multi-layer cryptography, and quantum-safe algorithms will form an integral part of the developed architecture.
The Swedish partners in AI-NET-PROTECT share the same vision that automation is a clear prerequisite for the efficient use of a heterogeneous and flexible network infrastructure which is programmable across all its components (i.e., from basic connectivity setup, to open and scalable node architectures and to fully virtualized network functions and application components). For this reason, the Swedish consortium of the AI-NET-PROTECT will research and develop technologies specific for an automated and secure communication infrastructure characterized by a mix of basic technologies for virtualization platforms and connectivity resources supporting critical services in customized network slices. To manage the resulting complexity, the Swedish consortium will take advantage of artificial intelligence (AI) and machine learning (ML) techniques to complement or replace traditional optimization and prediction algorithms. Key use cases for the use of AI/ML will be: performance optimization, proactive fault and anomaly detection, penetration and vulnerability testing, and security incident management.

Deltagare

Paolo Monti (kontakt)

Biträdande professor vid Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Ehsan Etezadi

Doktorand vid Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Marija Furdek Prekratic

Forskarassistent vid Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Rui Lin

Forskare vid Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Carlos Natalino Da Silva

Forskare vid Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Lena Wosinska

Forskningsprofessor vid Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Samarbetspartners

Försvarets Materielverk (FMV)

Stockholm, Sweden

Lunet AB

Luleå, Sweden

RISE Research Institutes of Sweden

Göteborg, Sweden

Savantic AB

Stockholm, Sweden

TELIA COMPANY AB

Sweden

Waystream AB

Stockholm, Sweden

Finansiering

VINNOVA

Projekt-id: 2020-03506
Finansierar Chalmers deltagande under 2021–2024

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Senast uppdaterat

2021-04-30