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  • 1.
    Arfaoui, Ghada
    et al.
    Orange, France.
    Bisson, Pascal
    Thales, France.
    Blom, Rolf
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Borgaonkar, Ravishankar
    University of Oxford, UK.
    Englund, Håkan
    Ericsson AB, Sweden.
    Felix, Edith
    Thales, France.
    Klaedtke, Felix
    NEC Labs Europe, Germany.
    Nakarmi, Prajwol K.
    Ericsson AB, Sweden.
    Naslund, Mats
    Ericsson AB, Sweden.
    O'Hanlon, Piers
    University of Oxford, UK.
    Papay, Juri
    IT Innovation Centre, UK.
    Suomalainen, Jani
    VTT Technical Research Centre of Finland, Finland.
    Surridge, Mike
    IT Innovation Centre, UK.
    Wary, Jean-Philippe
    Orange, France.
    Zahariev, Alexander
    Nixu Corporation, Finland.
    A Security Architecture for 5G Networks2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, no 17, p. 22466-22479Article in journal (Refereed)
    Abstract [en]

    5G networks will provide opportunities for the creation of new services, for new business models, and for new players to enter the mobile market. The networks will support efficient and cost-effective launch of a multitude of services, tailored for different vertical markets having varying service and security requirements, and involving a large number of actors. Key technology concepts are network slicing and network softwarisation, including network function virtualisation and software-defined networking. The presented security architecture builds upon concepts from the 3G and 4G security architectures but extends and enhances them to cover the new 5G environment. It comprises a toolbox for security relevant modelling of the systems, a set of security design principles, and a set of security functions and mechanisms to implement the security controls needed to achieve stated security objectives. In a smart city use case setting, we illustrate its utility; we examine the high-level security aspects stemming from the deployment of large numbers of IoT devices and network softwarisation.

  • 2.
    Blom, Rolf
    et al.
    RISE, Swedish ICT, SICS.
    Schwarz, Oliver
    RISE, Swedish ICT, SICS, Security Lab.
    High Assurance Security Products on COTS Platforms2015In: ERCIM News, ISSN 0926-4981, E-ISSN 1564-0094, no 102, p. 39-40Article in journal (Refereed)
    Abstract [en]

    With commodity operating systems failing to establish unbreakable isolation of processes, there is a need for stronger separation mechanisms. A recently launched open source project aims at applying virtualization to achieve such isolation on the widespread embedded ARM architectures. Strong assurance is established by formal verification and common criteria certification. Coexisting guest systems are able to run unmodified on the multicore platform, in a resource and cost efficient manner. The solution is rounded anchored in a secure boot process.

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  • 3.
    Ray, Apala
    et al.
    ABB, Sweden; Mälardalen University, Sweden.
    Åkerberg, Johan
    ABB, Sweden; Mälardalen University, Sweden.
    Björkman, Mats
    Mälardalen University, Sweden.
    Blom, Rolf
    RISE, Swedish ICT, SICS.
    Gidlund, Mikael
    Mid Sweden University, Sweden.
    Applicability of LTE Public Key Infrastructure Based Device Authentication in Industrial Plants2015In: Proceedings - IEEE Computer Society's International Computer Software and Applications Conference, 2015, 9, p. 510-515, article id 7273660Conference paper (Refereed)
    Abstract [en]

    The security in industrial automation domain using cryptography mechanisms is being discussed in both industry and academia. An efficient key management system is required to support cryptography for both symmetric key and public/private key encryption. The key management should ensure that the device is verified before distributing the initial key parameters to devices. The software/firmware used in the device comes from manufacturers, therefore the initial authenticity of the device can be easily verified with the help of manufacturers. Mobile telecommunication is an industrial segment where wireless devices are being used for a long time and the security of the wireless device management has been considered through a standard driven approach. Therefore, it is interesting to analyse the security authentication mechanisms used in mobile communication, specified in Long-Term-Evolution (LTE) standard. This paper analyses the initial device authentication using public key infrastructure in LTE standard, and discusses if, where and how the studied solutions can be tailored for device authenticity verification in industrial plant automation systems.

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    FULLTEXT01
  • 4.
    Zenden, Ivo
    et al.
    RISE Research Institutes of Sweden.
    Wang, Han
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Iacovazzi, Alfonso
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Vahidi, Arash
    RISE Research Institutes of Sweden.
    Blom, Rolf
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Raza, Shahid
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    On the Resilience of Machine Learning-Based IDS for Automotive Networks2023In: proc of IEEE Vehicular Networking Conference, VNC, IEEE Computer Society , 2023, p. 239-246Conference paper (Refereed)
    Abstract [en]

    Modern automotive functions are controlled by a large number of small computers called electronic control units (ECUs). These functions span from safety-critical autonomous driving to comfort and infotainment. ECUs communicate with one another over multiple internal networks using different technologies. Some, such as Controller Area Network (CAN), are very simple and provide minimal or no security services. Machine learning techniques can be used to detect anomalous activities in such networks. However, it is necessary that these machine learning techniques are not prone to adversarial attacks. In this paper, we investigate adversarial sample vulnerabilities in four different machine learning-based intrusion detection systems for automotive networks. We show that adversarial samples negatively impact three of the four studied solutions. Furthermore, we analyze transferability of adversarial samples between different systems. We also investigate detection performance and the attack success rate after using adversarial samples in the training. After analyzing these results, we discuss whether current solutions are mature enough for a use in modern vehicles.

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    fulltext
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