The objective of the COMPLETE project is to optimize spending of public resources when purchasing network equipment and the related services. As a key approach towards this goal, the project proposes to create a common information platform for public procurers and support them in the procurement process by providing the organizational and technical expertise. Required information under such platform will be gathered from the biggest vendors of optical transport system equipment. Relations with mentioned key vendors need to be established, sustained and promoted. Such platform will significantly improve the quality of decisions taken by public procurers and in some cases would enable to conduct such procurement that previously would not have been possible due to various organizational and technological constraints (for example lack of access to specific technical information and roadmaps). Gathered and sustained information under the platform will be accurate, measurable and comparable as it would be easy for the reader to understand each vendor’s positioning regarding particular technical features. Project will directly coordinate procurement procedures in respect of optical network solutions for the National Research and Education Network environment and public entities. It will support public bodies in procurement procedures and indicate how the procurements can be synchronized with other European Commission programs. Another objective is to define common procurement procedures to help NRENs with the whole process of purchasing new optical networking equipment. There is a number of Horizon 2020 program activities for research, innovation and entrepreneurship that are linked with the project. The results achieved under this project’s proposal will be especially useful for the following two ICT calls: • ICT-27-2015 • ICT-36-2015 • ICT-06-2014

BEBA challenges a very ambitious goal: can we deploy wire-speed-reactive control/processing tasks inside the network switches, while retaining i) centralized control of their specification/operation, ii) high performance and scalability, and - crucial for real world adoption - iii) platform independency, i.e., consistency with the vendors’ need for closed platforms? BEBA’s answer revolves around the identification of a programming abstraction in the form of eXtended Finite State Machines (XFSMs). Via platform-agnostic XFSM “programs”, operators and enterprises will deploy not only static packet forwarding/processing rules (as in current OpenFlow devices), but will be able to specify and wire-speed enforce how such rules shall dynamically adapt to the time-varying flow and traffic behavior, i.e. in reaction to packet-level events, internal statistics changes, link/queue conditions, etc. BEBA holds the promise for future-proof, efficient, and easy to administer network devices capable to be repurposed so as to meet emerging needs. Indeed, BEBA’s approach, extended with node-level processing primitives made usable through our programming abstraction, brings about key benefits in the ability to i) “software-define” middlebox-type network functions well beyond static packet forwarding, with specific project’s attention to the monitoring and network security applications’ domains, and in the possibility to ii) address the current shortcomings revolving around the high latency and large overhead of centralized network control tasks. Finally, BEBA commits to a concrete and pragmatic innovation strategy. Short-time impact and standardization will be attempted by i) casting (part of) the BEBA approach as OpenFlow’s extensions; ii) making its implementation feasible over merchant-silicon chipsets currently found in commodity switches, and iii) releasing an open source virtual BEBA switch for the benefits of the Network Functions Virtualization community.

High Performance Language Technologies (HPLT) is a space combining petabytes of natural language data with large-scale model training. With trillions of words of text, the space will be the largest open text collection. Cleaning and privacy protecting services improve the quality and ethical properties of the text. Going beyond static repositories that require the user to individually analyze each data set, the project will rate data sets by how much they improve end-to-end language models and machine translation systems. Continuous integration of models and data will result in free downloadable high-quality models for all official European Union languages and beyond. The models will be reproducible with information and evaluation metrics shown in a publicly available dashboard. By focusing on training at scale, the project complements the inference-focused European Language Grid, which in turn will be used for model deployment. Datasets, models and information about them will be published in recognized FAIR data repositories, aggregation catalogues and marketplaces for easy discovery, access, replication, and exploitation.

SAPPAN aims to develop a platform for sharing and automation to enable privacy preserving and efficient response and recovery utilizing advanced data analysis and machine learning. SAPPAN will provide a cyber threat intelligence system that decreases the effort required by a security analyst to find optimal responses to and ways to recover from an attack. SAPPAN will enable this within a single organization as well as across organisations through novel models for privacy-preserving data processing and sharing. It will enable utilizing external experts for intrusion detection and sharing of knowledge on response and recovery actions while respecting the privacy and confidentiality requirements of individuals and organizations. SAPPAN will enable a European level perspective on advanced cyber security threats detection, response, and recovery making four key contributions that go beyond existing approaches: (1) privacy-preserving aggregation and data analytics including advanced client-side abstractions; (2) federated threat detection based on sharing of anonymised data and sharing of trained machine learning models; (3) standardisation of knowledge in the context of incident response and recovery to enable reuse and sharing; (4) visual, interactive support for Security Operation Center operators. SAPPAN aims to provide solutions for public international institutions and multinational companies who want to enrich their Situational Awareness by sharing cyber security intelligence as well as solutions for small and midsize companies enabling them to outsource intrusion detection. SAPPAN will be demonstrated in the relevant environments of 2 multinational companies, 1 National Research and Education Network (NREN) and 2 Computer Security Incident Response Teams (CSIRT). The consortium consists of 1 NREN, 3 multinational companies, 3 universities and 1 research institute so as to maximise the technical and societal impact, the dissemination and uptake of the results.