DIT - RYSC - Comunicaciones en Conferencias Internacionales / International Conferences

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Deployment of Secure Machine Learning Pipelines for Near-Real-Time Control of 6G Network Services
(2023) González, Pol; Zahir, Adam; Grasselli, Chiara; Muñiz, Alejandro; Ggroshev, Milan; Barzegar, Sima; Callegati, Franco; Careglio, Davide; Ruiz, Marc; Velasco, Luis; European Commission
A ML function orchestrator deploying secure ML pipelines to support near-real-time control of network services is demonstrated. A distributed ledger supports the initial key exchange to establish secure connectivity among the agents in the pipeline
Orchestration Procedures for the Network Intelligence Stratum in 6G Networks
(IEEE, 2023-06-06) Chatzieleftheriou, Livia Elena; Gramaglia, Marco; Camelo, Miguel; García-Saavedra, Andrés; Kosmatos, Evangelos; Gucciardo, Michele; Soto, Paola; Iosifidis, George; Fuentes, Lidia; García Avilés, Ginés; Lutu, Andra Elena; Baldoni, Gabriele; Fiore, Marco; European Commission
The quest for autonomous mobile networks introduces the need for fully native support for Network Intelligence (NI) algorithms, typically based on Artificial Intelligence tools like Machine Learning, which shall be gathered into a NI stratum. The NI stratum is responsible for the full automation of the NI operation in the network, including the management of the life-cycle of NI algorithms, in a way that is synergic with traditional network management and orchestration framework. In this regard, the NI stratum must accommodate the unique requirements of NI algorithms, which differ from the ones of, e.g., virtual network functions, and thus plays a critical role in the native integration of NI into current network architectures. In this paper, we leverage the recently proposed concept of Network Intelligence Orchestrator (NIO) to (i) define the specific requirements of NI algorithms, and (ii) discuss the procedures that shall be supported by an NIO sitting in the NI stratum to effectively manage NI algorithms. We then (iii) introduce a reference implementation of the NIO defined above using cloud-native open-source tools.
Using CTI Data to Understand Real World Cyberattacks
(IEEE, 2023-03-23) Allegretta, Mauro; Siracusano, Giuseppe; González Sánchez, Roberto; Vallina Rodríguez, Pelayo; Gramaglia, Marco; European Commission; Ministerio de Asuntos Económicos y Transformación Digital (España)
The forensic analysis of Cyber Threat Intelligence (CTI) data is of capital importance for businesses and enterprises to understand what has possibly gone wrong in a cybersecurity system. Moreover, the fast evolution of the techniques used by cybercriminals requires collaboration among multiple partners to provide efficient security mechanisms. STIX has emerged as the industrial standard to share CTI data in a structured format, allowing entities from over the world to exchange information to broaden the knowledge base in the area. In this work, we shed light on the type of information contained in these datasets shared among partners. We analyze a large real-world STIX dataset and identify trends for the reporting of CTI data. Then, we deep dive into two kinds of attack patterns found in the dataset: Command & Control and Malicious Software Download. We found the data is not only useful for forensic analysis but can also be used to improve the protection against new attacks.
Multi-interface network framework for UAV management and data communications
(IEEE, 2022-01-24) Sánchez Agüero, Víctor; Fas-Millán, Miguel Ángel; Valera Pintor, Francisco; Vidal Fernández, Iván; Paniagua Tineo, Alejandro; López da Silva, Rafael A.; Manjón, José Manuel; Comunidad de Madrid; Agencia Estatal de Investigación (España); European Commission
Recent efforts to manage Unmanned Aerial Vehicle (UAV) operations in European civilian environments have resulted in the development of U-space, the European Union’s UAS Traffic Management (UTM) concept of operations. This paper presents the primary purposes of the H2020 Labyrinth project (mainly focusing on the communications architecture), which has as its main challenge to create and validate UAV applications through the research and development of path-planning algorithms and new UTM services. In addition, this article performs a preliminary validation of a communications prototype (including three communication alternatives) with real equipment of the National Institute of Aerospace Technology (INTA) of the Spanish Ministry of Defense. The presented results show the functionality of the prototypes and serve as a starting point to develop the requirements defined in the communications architecture.
5G Exchange (5GEx) - Multi-domain Orchestration for Software Defined Infrastructures
(2015-07-29) Bernardos Cano, Carlos Jesús; Dugeon, Olivier; Galis, Alex; Morris, Donal; Simon, Csaba; Szabo, Robert
Market fragmentation has resulted in a multitude of network and cloud/data centre operators each focused on different countries, regions and technologies. This makes it difficult and costly to create infrastructure services spanning multiple countries, such as virtual connectivity or compute resources, as no single operator has a footprint everywhere. The goal of the 5G Exchange (5GEx) project is to enable crossdomain orchestration of services over multiple administrations or over multi-domain single administrations in the context of emerging 5G Networking. This will allow end-to-end network and service elements to mix in multi-vendor, heterogeneous technology and resource environments. 5GEx aims to enable collaboration between operators, regarding 5G infrastructure services, with the view to introducing a unification via NFV/SDN compatible software defined infrastructure multi-domain orchestration for networks, clouds and services.
A virtualization approach to validate services and subsystems of a MALE UAS
(IEEE, 2022-05-02) Sánchez Agüero, Víctor; Valera Pintor, Francisco; Vidal Fernández, Iván; Nogales Dorado, Borja; Cabezas, Jaime; Vidal Bustos, Carlos; Comunidad de Madrid; European Commission; Ministerio de Ciencia e Innovación (España); Universidad Carlos III de Madrid
Current trends in Unmanned Aircraft Systems (UAS) aim at embedding intelligent functionality into these devices to enhance their utilization beyond their traditional video monitoring and recording operations, using them as complex mobile computing platforms or to support flexible communication network infrastructures. As a consequence, while UAS necessarily increase their complexity, the whole development cycle of services/applications of UAS, together with the management, operation, testing, validation, and maintenance, becomes even more challenging since the computing hardware has to be onboarded and ready to fly. This article presents a Medium Altitude Long Endurance (MALE) surveillance UAS developed by the Spanish Ministry of Defense (the MILANO, from the National Institute of Aerospace Technology), used as a reference for the developments in this paper. The article also presents the virtualization platform that is being used in the system to facilitate the deployment of all the communication service components in the real MILANO platform (e.g., routing, switching, channel selection functionalities), although it also supports other types of applications (e.g., telemetry preprocessing, including sensors and video, route planning). In addition, this platform is complemented by an emulation infrastructure (to reproduce different mobility patterns and radio communication link status) used to boost the whole testing and validation cycle before onboarding the hardware and scheduling expensive flight campaigns, and facilitating the overall UAS software maintenance during the operations phase. Finally, the article describes the experiments performed with the MILANO equipment, validating the application and functionality of the overall platform.
DAEMON: A network intelligence plane for 6G networks
(IEEE, 2022-12-04) Camelo, Miguel; Gramaglia, Marco; Soto, Paola; Fuentes, Lidia; Ballesteros, Joaquín; Bazco-Nogueras, Antonio; García Avilés, Ginés; Latré, Steven; García Saavedra, Andrés; Fiore, Marco; European Commission
While there is a clear trend towards network automation through the usage of Artificial Intelligence (AI) and Machine Learning (ML) solutions, the major reference network architectures are still not natively including all the mechanisms needed to handle Network Intelligence (NI). This paper introduces a novel architecture proposed within the EU-funded DAEMON project, which includes a Network Intelligence Plane (NIP) that natively integrates NI into the network operation, management, and orchestration procedures. We do so by analyzing the gaps in current reference architectures and designing a Network Intelligence Orchestration (NIO) that handles the most important NI-related mechanisms such as lifecycle management, coordination, and data management.
Forecasting for Network Management with Joint Statistical Modelling and Machine Learning
(IEEE, 2022-06-14) Lo Schiavo, Leonardo; Fiore, Marco; Gramaglia, Marco; Banchs Roca, Albert; Costa-Pérez, Xavier; European Commission; Ministerio de Asuntos Económicos y Transformación Digital (España)
Forecasting is a task of ever increasing importance for the operation of mobile networks, where it supports anticipa tory decisions by network intelligence and enables emerging zero touch service and network management models. While current trends in forecasting for anticipatory networking lean towards the systematic adoption of models that are purely based on deep learning approaches, we pave the way for a different strategy to the design of predictors for mobile network environments. Specifically, following recent advances in time series prediction, we consider a hybrid approach that blends statistical modelling and machine learning by means of a joint training process of the two methods. By tailoring this mixed forecasting engine to the specific requirements of network traffic demands, we develop a Thresholded Exponential Smoothing and Recurrent Neural Network (TES-RNN) model. We experiment with TES RNN in two practical network management use cases, i.e., (i) anticipatory allocation of network resources, and (ii) mobile traffic anomaly prediction. Results obtained with extensive traffic workloads collected in an operational mobile network show that TES-RNN can yield substantial performance gains over current state-of-the-art predictors in both applications considered
Network Intelligence for Virtualized RAN Orchestration: The DAEMON Approach
(IEEE, 2022-06-07) Gramaglia, Marco; Camelo, Miguel; Fuentes, Lidia; Ballesteros, Joaquín; Baldoni, Gabriele; Cominardi, Luca; García Saavedra, Andrés; Fiore, Marco; European Commission
Next-generation mobile networks will largely benefit from advances in softwarization and cloudification of network functions. However, fully exploiting the new potential of flexible network architectures in front of increasingly demanding service volumes and requirements calls for an extremely effective integration of Network Intelligence (NI) solutions into production infrastructures.While current standardization efforts towards embedding NI in beyond-5G and 6G systems are still in their infancy, the DAEMON project is developing technologies for a NI-native generation of mobile networks.In this paper, we present current evolutions proposed by DAEMON in terms of a general model for the representation of NI instances, which facilitates their synergic integration in network environments. We showcase the practical viability and advantages of the proposed approach with two state-of-the-art NI algorithms for vRAN orchestration implemented into an open-source data flow programming framework.
Nuberu : Reliable RAN Virtualization in Shared Platforms
(ACM, 2022-01-31) García Avilés, Ginés; García Saavedra, Andrés; Gramaglia, Marco; Costa Pérez, Ana; Serrano Yáñez-Mingot, Pablo; Banchs Roca, Albert; European Commission
RAN virtualization will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. Indeed, we show in this paper that the baseline architecture of a base station¿s distributed unit (DU) collapses upon moments of deficit in computing capacity. Recent solutions to accelerate some signal processing tasks certainly help but do not tackle the core problem: a DU pipeline that requires predictable computing to provide carrier-grade reliability. We present Nuberu, a novel pipeline architecture for 4G/5G DUs specifically engineered for non-deterministic computing platforms. Our design has one key objective to attain reliability: to guarantee a minimum set of signals that preserve synchronization between the DU and its users during computing capacity shortages and, provided this, maximize network throughput. To this end, we use techniques such as tight deadline control, jitter-absorbing buffers, predictive HARQ, and congestion control. Using an experimental prototype, we show that Nuberu attains 95% of the theoretical spectrum efficiency in hostile environments, where state-of-art approaches lose connectivity, and at least 80% resource savings
Demo: Nuberu - A Reliable DU DesignSuitable for Virtualization Platforms
(ACM, 2022-03-28) García Avilés, Ginés; García Saavedra, Andrés; Gramaglia, Marco; Costa-Pérez, Xavier; Serrano Yáñez-Mingot, Pablo; Banchs Roca, Albert; European Commission
We demonstrate Nuberu. The scenario consists of a DU under test (DuT), and one or more DUs sharing computing resources. A dashboard lets us control the type of DuT: Baseline, implemented with vanilla srsRAN, or Nuberu; the number of competing vDUs; and their SNR. A second screen shows real-time metrics: the processing latency of the TBs from each vDU instance; the throughput performance of DuT; the processing latency of DU jobs from DuT; and the ratio of latency constraint violations of DuT jobs. We show how the throughput attained by the baseline DU approach collapses upon sufficiently high computing interference from the competing DUs. Conversely, we show that the DU design introduced in [3] preserves reliability irrespective of the computing interference.
Requirements and Specifications for the Orchestration of Network Intelligence in 6G
(IEEE, 2022-01-08) Camelo, Miguel; Cominardi, Luca; Gramaglia, Marco; Fiore, Marco; García Saavedra, Andrés; Fuentes, Lidia; Soto, Paola; Slamnik-Kriještorac, Nina; Ballesteros, Joaquín; Chang, Chia-Yu; Baldoni, Gabriele; Márquez-Barja, Johann M.; Hellinckx, Peter; Latré, Steven; European Commission
Next-generation mobile networks are expected to flaunt highly (if not fully) automated management. Network Intelligence (NI) will be the key enabler for such a vision, empowering myriad of orchestrators and controllers across network domains. In this paper, we elaborate on the DAEMON architectural model, which proposes introducing a NI Orchestration layer for the effective end-to-end coordination of NI instances deployed across the whole mobile network infrastructure. Specifically, we first outline requirements and specifications for NI design that stem from data management, control timescales, and network technology characteristics. Then, we build on such analysis to derive initial principles for the design of the NI Orchestration layer, focusing on (i) proposals for the interaction loop between NI instances and the NI Orchestrator, and (ii) a unified representation of NI algorithms based on an extended MAPE-K model. Our work contributes to the definition of the interfaces and operation of a NI Orchestration layer that foster a native integration of NI in mobile network architectures.
Informing protocol design through crowdsourcing: The case of pervasive encryption
(Association For Computing Machinery (ACM), 2015-08-17) Mandalari, Anna María; Bagnulo Braun, Marcelo Gabriel; Lutu, Andra Elena
Middleboxes, such as proxies, firewalls and NATs play an important role in the modern Internet ecosystem. On one hand, they perform advanced functions, e.g. traffic shaping, security or enhancing application performance. On the other hand, they turn the Internet into a hostile ecosystem for innovation, as they limit the deviation from deployed protocols. It is therefore essential, when designing a new protocol, to first understand its interaction with the elements of the path. The emerging area of crowdsourcing solutions can help to shed light on this issue. Such approach allows us to reach large and different sets of users and also different types of devices and networks to perform Internet measurements. In this paper, we show how to make informed protocol design choices by using a crowdsourcing platform. We consider a specific use case, namely the case of pervasive encryption in the modern Internet. Given the latest public disclosures of the NSA global surveillance operations, the issue of privacy in the Internet became of paramount importance. Internet community efforts are thus underway to increase the adoption of encryption. Using a crowdsourcing approach, we perform large-scale TLS measurements to advance our understanding on whether wide adoption of encryption is possible in today’s Internet.
Trading accuracy for privacy in machine learning tasks: an empirical analysis
(IEEE, 2022-03-22) Prodomo, Vittorio; González Sánchez, Roberto; Gramaglia, Marco; European Commission
Different kinds of user-generated data are increasingly used to tailor and optimize, through Machine Learning, the operation of online services and infrastructures. This typically requires sharing data among different partners, often including private data of individuals or business confidential data. While this poses privacy issues, the current state-of-the-art solutions either impose strong assumptions on the usage scenario or drastically reduce the data quality. In this paper, we evaluate through a generic framework the trade-offs between the accuracy of Machine Learning tasks and the achieved privacy (measured as similarity) on the input data, discussing trends and ways forward.
5Growth data-driven AI-based scaling
(IEEE, 2021-06-08) De Vleeschauwer, Danny; Baranda, Jorge; Mangues-Bafalluy, Josep; Chiasserini, Carla Fabiana; Malinverno, Marco; Puligheddu, Corrado; Magoula, Lina; Martín Pérez, Jorge; Barmpounakis, Sokratis; Kondepu, Koteswararao; Valcarenzhi, Luca; Li, Xi; Papagianni, Chrysa; García Saavedra, Andrés; European Commission
This paper presents a data-driven approach leveraging AI/ML models to automate the service scaling operation and, in this way, meet the service requirements while minimizing the consumption of network, computing, and storage resources. This approach is integrated into the 5Growth service management software platform. In particular, a prototype was developed to demonstrate how the novel 5Growth AI/ML platform can be used in a closed-loop automation system to support the automated service scaling operation. Furthermore, a number of additional ML-based approaches are developed in the context of eMBB and C-V2N scenarios, which can be embedded into the system for handling more complex use cases.
COTORRA: Context-Aware testbed for robotic applications
(Association for Computing Machinery (ACM), 2021-06-25) Groshev, Milan; Martín Pérez, Jorge; Antevski, Kiril; Oliva Delgado, Antonio de la; Bernardos Cano, Carlos Jesús; European Commission
Edge computing have received considerable attention as a promising candidate for the evolution of robotic systems. In this work, we propose COTORRA, an Edge driven robotic testbed that combines context information with robot sensor data to validate innovative concepts for robotic systems prior to being applied in a production environment. We have tested COTORRA in a controlled university environment as an easy applicable, serverless, and modular testbed on top of commodity network infrastructure. COTORRA supports pluggable robotic applications. To verify its feasibility and assess its performance, we ran a set of experiments that show how autonomous navigation applications can achieve target latencies bellow 15 ms, and perform an inter-domain Distributed Ledger Technology (DLT) federation within 19 seconds.
PLIO: Physical Layer Identification using One-shot Learning
(IEEE, 2021-10-04) Hazra, Saptarshi; Voigt, Thiemo; Yan, Wenqing; European Commission
The Internet of Things (IoT) is connecting a massive scale of everyday objects to the internet. We need to ensure the secure connectivity and authentication of these devices. Physical (PHY)-layer identification methods can distinguish between different devices by leveraging their unique hardware imperfections. But these methods typically require large quantities of training data which makes them impractical for large deployment scenarios. Also, these methods do not address the PHY-layer identification of new devices joining an IoT network. In this paper, we propose a PHY-layer identification method using one-shot learning that can identify new devices using the network solicitation packet of the devices as reference packets. We show that our method can accurately identify new devices without training, achieving a precision and recall over 80% even in the presence of 10 dBm noise. Furthermore, we show that with minimal retraining using only three packets from each device, we can accurately identify all devices in the IoT network with a precision and recall of 93%.
OpenFlowMon: a fully distributed monitoring framework for virtualized environments
(IEEE, 2021-11-09) Cobos Dominguez, Antonio; Magalhaes Guimaraes, Carlos Eduardo; Oliva Delgado, Antonio de la; Zabala Orive, Aitor; European Commission
Network monitoring allows a continuous assessment on the health and performance of the network infrastructure. With the significant change on how networks are deployed and operated, mainly due to the advent of virtualization technologies, alternative monitoring approaches are emerging to provide a finer-grained flow monitoring to complement already existing mechanisms and capabilities. In this paper, we proposed and developed an Open-Source Flow Monitoring Framework (OpenFlowMon), a fully distributed monitoring framework implemented solely with open-source solutions. This framework is used to assess the performance and the overhead introduced by two different flow monitoring approaches: (i) switch level and (ii) compute node level monitoring. Results show that monitoring at compute node level not only reduces the overhead but also mitigates a potential complex post-processing in east-to-west traffic.
Demo: AIML-as-a-service for SLA management of a digital twin virtual network service
(IEEE, 2021-05-10) Baranda, Jorge; Zeydan, Engin; Casetti, C.; Chiasserini, C. F.; Malinverno, Marco; Puligheddu, C.; Groshev, Milan; Magalhaes Guimaraes, Carlos Eduardo; Tomakh, K.; Kucherenko, D.; Kolodiaznhyi, O.; Mangues-Bafalluy, Josep; European Commission
This demonstration presents an AI/ML platform that is offered as a service (AIMLaaS) and integrated in the management and orchestration (MANO) workflow defined in the project 5Growth following the recommendations of various standardization organizations. In such a system, SLA management decisions (scaling, in this demo) are taken at runtime by AI/ML models that are requested and downloaded by the MANO stack from the AI/ML platform at instantiation time, according to the service definition. Relevant metrics to be injected into the model are also automatically configured so that they are collected, ingested, and consumed along the deployed data engineering pipeline. The use case to which it is applied is a digital twin service, whose control and motion planning function has stringent latency constraints (directly linked to its CPU consumption), eventually determining the need for scaling out/in to fulfill the SLA.
Data collection and utilization framework for edge AI applications
(IEEE, 2021-07-08) Rexha, Hergys; Lafond, Sébastien; European Commission
As data being produced by IoT applications continues to explode, there is a growing need to bring computing power closer to the source of the data to meet the response-time, power dissipation and cost goals of performance-critical applications in various domains like Industrial Internet of Things (IIoT), Automated Driving, Medical Imaging or Surveillance among others. This paper proposes a data collection and utilization framework that allows runtime platform and application data to be sent to an edge and cloud system via data collection agents running close to the platform. Agents are connected to a cloud system able to train AI models to improve overall energy efficiency of an AI application executed on a edge platform. In the implementation part we show the benefits of FPGA-based platform for the task of object detection. Furthermore we show that it is feasible to collect relevant data from an FPGA platform, transmit the data to a cloud system for processing and receiving feedback actions to execute an edge AI application energy efficiently. As future work we foresee the possibility to train, deploy and continuously improve a base model able to efficiently adapt the execution of edge applications.

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