Recent Submissions

  • A first look into Alexa’s interaction security 

    Castell Uroz, Ismael; Marrugat Plaza, Xavier; Solé Pareta, Josep; Barlet Ros, Pere (Association for Computing Machinery (ACM), 2019)
    Conference report
    Open Access
    With a rapidly increasing market of millions of devices, the intelligent virtual assistants (IVA) have become a new vector available to exploit security breaches. In this work we approach the third revision of the Amazon ...
  • Brown-field gradual migration planning toward spectrally-spatially flexible optical networks 

    Lechowicz, Piotr; Perelló Muntan, Jordi; Spadaro, Salvatore; Walkowiak, Krzysztof (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    Conference report
    Restricted access - publisher's policy
    In this paper, we motivate a brown-field migrationplanning as a cost-efficient procedure to scale the capacity ofshort-term realizable elastic optical networks (EONs), graduallyconverting them into ...
  • Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN 

    Rusek, Krzysztof; Suárez-Varela Maciá, José Rafael; Mestres Sugrañes, Albert; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2019)
    Conference report
    Open Access
    Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques ...
  • Feature engineering for deep reinforcement learning based routing 

    Suárez-Varela Maciá, José Rafael; Mestres Sugrañes, Albert; Yu, Junlin; Kuang, Li; Feng, Haoyu; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Institute of Electrical and Electronics Engineers (IEEE), 2019)
    Conference report
    Open Access
    Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a dramatic improvement in decision-making and automated control problems. As a result, we are witnessing a growing number of research works that ...
  • Towards more realistic network models based on Graph Neural Networks 

    Badia Sampera, Arnau; Suárez-Varela Maciá, José Rafael; Almasan Puscas, Felician Paul; Rusek, Krzysztof; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2019)
    Conference report
    Open Access
    Recently, a Graph Neural Network (GNN) model called RouteNet was proposed as an efficient method to estimate end-to-end network performance metrics such as delay or jitter, given the topology, routing, and traffic of the ...
  • Challenging the generalization capabilities of Graph Neural Networks for network modeling 

    Suárez-Varela Maciá, José Rafael; Carol Bosch, Sergi; Rusek, Krzysztof; Almasan Puscas, Felician Paul; Arias Vicente, Marta; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2019)
    Conference report
    Open Access
    Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay or jitter) at limited cost. Recently a novel Graph Neural Network (GNN) ...
  • Detecting cryptocurrency miners with NetFlow/IPFIX network measurements 

    Zayuelas Muñoz, Jordi; Suárez-Varela Maciá, José Rafael; Barlet Ros, Pere (Institute of Electrical and Electronics Engineers (IEEE), 2019)
    Conference report
    Open Access
    In the last few years, cryptocurrency mining has become more and more important on the Internet activity and nowadays is even having a noticeable impact on the global economy. This has motivated the emergence of a new ...
  • A NetFlow/IPFIX implementation with OpenFlow 

    Suárez-Varela Maciá, José Rafael; Barlet Ros, Pere; Carela Español, Valentín (2017)
    Conference report
    Open Access
  • Scalability of network capacity in nanonetworks powered by energy harvesting 

    Cid-Fuentes, Raül G.; Abadal Cavallé, Sergi; Cabellos Aparicio, Alberto; Alarcón Cot, Eduardo José (Association for Computing Machinery (ACM), 2015)
    Conference report
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    This paper provides design guidelines in the feasibility and deployability of nanonetworks powered by energy harvesting techniques throughout bounding the per node throughput capacity as a function of the number of nodes. ...
  • Graphene-enabled wireless networks-on-chip 

    Llatser Martí, Ignacio; Abadal Cavallé, Sergi; Mestres Sugrañes, Albert; Cabellos Aparicio, Alberto; Alarcón Cot, Eduardo José (Institute of Electrical and Electronics Engineers (IEEE), 2013)
    Conference report
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    Graphene-enabled Wireless Communications (GWC) advocate for the use of graphene-based plasmonic antennas, or graphennas, which take advantage of the plasmonic properties of graphene to radiate electromagnetic waves in the ...
  • Initial MAC exploration for graphene-enabled wireless networks-on-chip 

    Piro, Giuseppe; Abadal Cavallé, Sergi; Mestres Sugrañes, Albert; Alarcón Cot, Eduardo José; Solé Pareta, Josep; Grieco, L. Alfredo; Boggia, Gennaro (Association for Computing Machinery (ACM), 2014)
    Conference report
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    In the upcoming many-core era, chip multiprocessor architectures will be composed of hundreds or even thousands of processor cores, which interact among them through an on-chip communication platform for synchronization ...
  • A vertical methodology for the design space exploration of graphene-enabled wireless communications 

    Abadal Cavallé, Sergi; Mestres Sugrañes, Albert; Llatser Martí, Ignacio; Alarcón Cot, Eduardo José; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2015)
    Conference report
    Restricted access - publisher's policy
    Graphene-based antennas (or shortly named, graphennas) are envisaged to be the cornerstone of novel wireless communication systems by virtue of their reduced size, in the micrometer range, and an expected radiation frequency ...

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