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Towards Incidence Management in 5G Based on Situational Awareness

Author

Listed:
  • Lorena Isabel Barona López

    (Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain
    These authors contributed equally to this work.)

  • Ángel Leonardo Valdivieso Caraguay

    (Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain
    These authors contributed equally to this work.)

  • Jorge Maestre Vidal

    (Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain
    These authors contributed equally to this work.)

  • Marco Antonio Sotelo Monge

    (Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain
    These authors contributed equally to this work.)

  • Luis Javier García Villalba

    (Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain
    These authors contributed equally to this work.)

Abstract

The fifth generation mobile network, or 5G, moves towards bringing solutions to deploying faster networks, with hundreds of thousands of simultaneous connections and massive data transfer. For this purpose, several emerging technologies are implemented, resulting in virtualization and self-organization of most of their components, which raises important challenges related to safety. In order to contribute to their resolution, this paper proposes a novel architecture for incident management on 5G. The approach combines the conventional risk management schemes with the Endsley Situational Awareness model, thus improving effectiveness in different aspects, among them the ability to adapt to complex and dynamical monitoring environments, and countermeasure tracking or the role of context when decision-making. The proposal takes into account all layers for information processing in 5G mobile networks, ranging from infrastructure to the actuators responsible for deploying corrective measures.

Suggested Citation

  • Lorena Isabel Barona López & Ángel Leonardo Valdivieso Caraguay & Jorge Maestre Vidal & Marco Antonio Sotelo Monge & Luis Javier García Villalba, 2017. "Towards Incidence Management in 5G Based on Situational Awareness," Future Internet, MDPI, vol. 9(1), pages 1-14, January.
    • Handle: RePEc:gam:jftint:v:9:y:2017:i:1:p:3-:d:88007
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    References listed on IDEAS

    as
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    2. Shin, Jinsoo & Son, Hanseong & Khalil ur, Rahman & Heo, Gyunyoung, 2015. "Development of a cyber security risk model using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 208-217.
    3. Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
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    Cited by:

    1. Jani Suomalainen & Kimmo Ahola & Mikko Majanen & Olli Mämmelä & Pekka Ruuska, 2018. "Security Awareness in Software-Defined Multi-Domain 5G Networks," Future Internet, MDPI, vol. 10(3), pages 1-24, March.

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