Quantum computers will change the cryptographic panorama. A technology once believed to lay far away into the future is increasingly closer to real world applications. Quantum computers will break the algorithms used in our public key infrastructure and in our key exchange protocols, forcing a complete retooling of the cryptography as we know it. Quantum Key distribution is a physical layer technology immune to quantum or classical computational threats. However, it requires a physical substrate, and optical fiber has been the usual choice. Most of the time used just as a point to point link for the exclusive transport of the delicate quantum signals. Its integration in a real-world shared network has not been attempted so far. Here we show how the new programmable software network architectures, together with specially designed quantum systems can be used to produce a network that integrates classical and quantum communications, including management, in a single, production-level infrastructure. The network can also incorporate new quantum-safe algorithms and use the existing security protocols, thus bridging the gap between today's network security and the quantum-safe network of the future. This can be done in an evolutionary way, without zero-day migrations and the corresponding upfront costs. We also present how the technologies have been deployed in practice using a production network.
The on-going digital transformation is key to progress towards a new generation of more efficient, sustainable and connected industrial systems allowing the so-called factories of the future. This new generation, commonly referred to as industry 4.0, will be accompanied by a new wave of use cases that will allow companies from logistics and manufacturing sectors to increase flexibility, productivity and usability in the industrial processes executed within their factory premises. Unlike typical use cases from other vertical sectors (e.g. energy, media, smart cities), industry 4.0 use cases will bring very stringent requirements in terms of latency, reliability and high-accuracy positioning. The combination of 5G technology with enterprise network solutions becomes crucial to satisfy these requirements in indoor, private environments. In this context, the concept of 5G non-public networks has emerged. In this article we provide an overview of 5G non-public networks, studying their applicability to the industry 4.0 ecosystem. On the basis of the work (being) developed in 3GPP Rel-16 specifications, we identify a number of deployment options relevant for non-public networks, and discuss their integration with mobile network operators' public networks. Finally, we provide a comparative analysis of these options, assessing their feasibility according to different criteria, including technical, regulatory and business aspects. The outcome of this analysis will help industry players interested in using non-public networks to decide which is the most appropriate deployment option for their use cases.
resources to the crypto-currency mining pools they benefit from. This research work focuses on offering a solution for detecting such abusive cryptomining activity, just by means of passive network monitoring. To this end, we identify a new set of highly relevant network flow features to be used jointly with a rich set of machine and deep-learning models for real-time cryptomining flow detection. We deployed a complex and realistic cryptomining scenario for training and testing machine and deep learning models, in which clients interact with real servers across the Internet and use encrypted connections. A complete set of experiments were carried out to demonstrate that, using a combination of these highly informative features with complex machine learning models, cryptomining attacks can be detected on the wire with telco-grade precision and accuracy, even if the traffic is encrypted.
It is expected that the fifth generation mobile networks (5G) will support both human-to-human and machine-to-machine communications, connecting up to trillions of devices and reaching formidable levels of complexity and traffic volume. This brings a new set of challenges for managing the network due to the diversity and the sheer size of the network. It will be necessary for the network to largely manage itself and deal with organisation, configuration, security, and optimisation issues. This paper proposes an architecture of an autonomic self-managing network based on Network Function Virtualization, which is capable of achieving or balancing objectives such as high QoS, low energy usage and operational efficiency. The main novelty of the architecture is the Cognitive Smart Engine introduced to enable Machine Learning, particularly (near) real-time learning, in order to dynamically adapt resources to the immediate requirements of the virtual network functions, while minimizing performance degradations to fulfill SLA requirements. This architecture is built within the CogNet European Horizon 2020 project, which refers to Cognitive Networks
The current device-centric protection model against security threats has serious limitations. On the one hand, the proliferation of user terminals such as smart-phones, tablets, notebooks, smart TVs, game consoles and desktop computers makes it extremely difficult to achieve the same level of protection regardless of the device used. On the other hand, when various users share devices (e.g., parents and kids using the same devices at home), the set up of distinct security profiles, policies, and protection rules for the different users of a terminal is far from trivial. In light of this, this paper advocates for a paradigm shift in user protection. In our model, the protection is decoupled from the users' terminals, and it is provided by the access network through a Trusted Virtual Domain (TVD). Each TVD provides unified and homogeneous security for a single user, irrespective of the terminal employed. We describe a user-centric model, where non-technically savvy users can define their own profiles and protection rules in an intuitive way. We show that our model can harness from the virtualization power offered by nextgeneration access networks, especially, from Network Functions Virtualization (NFV) in the Points of Presence (POPs) at the edge of Telecom operators. We also analyze the distinctive features of our model, and the challenges faced based on the experience gained in the development of a proof-of-concept.
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