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International audience; The fifth generation of mobile broadband is more than just an evolution to provide more mobile bandwidth, massive machine-type communications, and ultra-reliable and low-latency communications. It relies on a complex, dynamic and heterogeneous environment that implies addressing numerous testing and security challenges. In this paper we present 5Greplay, an open-source 5G network traffic fuzzer that enables the evaluation of 5G components by replaying and modifying 5G network traffic by creating and injecting network scenarios into a target that can be a 5G core service (e.g., AMF, SMF) or a RAN network (e.g., gNodeB). The tool provides the ability to alter network packets online or offline in both control and data planes in a very flexible manner. The experimental evaluation conducted against open-source based 5G platforms, showed that the target services accept traffic being altered by the tool, and that it can reach up to 9.56 Gbps using only 1 processor core to replay 5G traffic.

Future drone delivery infrastructure and service for distributing goods to inner city and rural areas, by collaborating with the already existing transportation system, has become an important function in the semi-/full-automation process of home deliveries. This has been exacerbated by the current (and possible future) Covid-19 pandemic. This paper presents a new paradigm for a 5G enabled drone delivery system based on ground-aerial integrated mobilities called TWIN that provide a high-quality communication system for increased drone fleet service capacity. We depict the 5G architecture that supports the dual mobilities and emphasize the strategies to ensure reliable, low latency radio link, high accuracy drone positioning together with location management. The increased link level availability coupled with high accuracy location management, allows for a series of new network applications and support the fast creation of drone delivery services. We envision that logistic services on the TWIN concept will have a high societal and economic impact for both urban and rural areas. acceptedVersion

openaire: EC/H2020/871808/EU//INSPIRE-5Gplus The promise of disparate features envisioned by the 3GPP for 5G, such as offering enhanced Mobile Broadband connectivity while providing massive Machine Type Communications likely with very low data rates and maintaining Ultra Reliable Low Latency Communications requirements, create a very challenging environment for protecting the 5G networks themselves and associated assets. To overcome such complexity, future 5G networks must employ a very high degree of network and service management automation, which is a security challenge by itself as well as an opportunity for smarter and more efficient security functions. In this paper, we present the smart, trustworthy and liable 5G security platform being designed and developed in the INSPIRE-5Gplus1 project. This platform takes advantage of new techniques such as Machine Learning (ML), Artificial Intelligence (AI), Distributed Ledger Technologies (DLT), network softwarization and Trusted Execution Environment (TEE) for closed-loop and end-to-end security management following a zero-touch model in 5G and Beyond 5G networks. To this end, we specifically elaborate on two key aspects of our platform, namely security management with Security Service Level Agreements (SSLAs) and liability management, in addition to the description of the overall architecture.

This Deliverable presents the current security landscape of 5G networks, as well as the evolution of requirements and trends in 5G security. It includes a summary of the 5G threat landscape, the 5G networks classification criteria and their threat taxonomy; a description of security requirements of 5G systems, divided into domain-specific use cases, and the elicitation of security requirements from relevant stakeholders in 5G; the current status of 5G networks, the solutions state for securing 5G systems, the standardization effort in the domain of 5G security, the relevant 5G projects, and open source initiatives; and a description of future trends and technologies in 5G networks, their limitations, and gaps related to the security of 5G networks. This deliverable aims to provide a basis for the identification of use cases and the development of 5G security enablers in INSPIRE-5Gplus.

International audience; Anomaly detection remains a challenging task due to both the ever more complex functions that need to be executed and the evolution of current networking devices which induces limitation of computational resources such as the Internet of Things (IoT). Furthermore, results of anomaly function computations can be repeated gradually over time or executed in neighboring nodes, thus leading to a waste of such limited computing resources in constrained nodes. To tackle these issues, the content-centric paradigm enhanced with computing features offers a promising solution to reduce the computation resources and finally improve the scalability of anomaly detection functions. In this paper, we propose a first step toward a content-oriented control plane which enables the distribution of the processing and the sharing of results of anomaly detection functions in the network. We present the way we leverage NFN to support Bayesian Network inference to detect anomalies in network traffic. The relevance and performance of our proposed approach are demonstrated by considering the Content Poisoning Attack (CPA) through numerous experiment data.

Part 6: Industrial Applications; International audience; This paper presents the main results of the experiments conducted using the MMT-IoT security analysis solution run on a IoT Fed4Fire+ platform (Virtual Wall - w.iLab proposed by IMEC, Belgium). MMT is a monitoring framework developed by Montimage, and MMT-IoT is the tool that allows monitoring and analysing the security and performance of IoT networks. The results obtained concern two principal advancements. First, the adaptations made to deploy MMT-IoT on the IoT platform in order to run the tool on the platform’s IoT devices. Second, the deployment of the software allowed us to run preliminary tests on the selected platform for performing initial validation and scalability tests on this real environment. To this end, Montimage defined and implemented three test scenarios related to security and scalability with 1 or more clients. These results will be used to prepare a new experimentation phase involving also another Fed4Fire+ platform (LOG-a-TEC proposed by IJS, Slovenia).

International audience; Named Data Networking (NDN) is a disruptive yet promising architecture for the future Internet, in which the content diffusion mechanisms are shifted from the conventional host-centric to content-centric ones so that the data delivery can be significantly improved. After a decade of research and development, NDN and the related NDN Forwarding Deamon (NFD) implementations are now mature enough to enable stake-holders, such as telcos, to consider them for a real deployment. Consequently, NDN and IP will likely cohabit, and the Future Internet may be formed of isolated administrative domains, each deploying one of these two network paradigms. The security question of the resulting architecture naturally arises. In this paper, we consider the case of Denial of Service. Even though the Interest Flooding Attack (IFA) has been largely studied and mitigated through NACK packets in pure NDN networks, we demonstrate in this paper through experimental assessments that there are still some ways to mount such an attack, and especially in the context of coupling NDN with IP, that can hardly be addressed by current solutions. Subsequently, we leverage hypothesis testing theory to develop a Generalized Likelihood Ratio Test (GLRT) adapted to evolved IFA attacks. Simulations show the relevance of the proposed model for guaranteeing the prescribed Probability of False Alarm (PFA) and highlights the trade-off between detection power and delay. Finally, we consider a real deployment scenario where NDN is coupled with IP to carry HTTP traffic. We show that the model of IFA attacks is not very accurate in practice and further develops a sequential detector to keep a high detection accuracy. By considering data from the testbed, we show the efficiency of the overall detection method.

International audience; After more than one decade of research efforts, ICN technologies seem mature enough to move from a design and implementation phase to early deployment trials. However, if one wants an ICN stack to be operated in a production context, some major locks, related to (1) the lack of adequate deployment infrastructure, (2) the migration of relevant services and (3) the capability to accurately monitor an overall ICN domain, must be addressed. In this paper we present a feedback experience of the deployment of a Named Data Networking island. The latter considers HTTP over NDN as a primary service deployed through dedicated gateways and NFV as a substrate for deploying and orchestrating ICN components. From the performance assessment of individual components up to the opening of the testbed to end-users in university campus, we propose an analysis that can guide further research efforts in the ICN area.

International audience; NDN is the most mature proposal of the ICN paradigm, a clean-slate approach for the Future Internet. Although NDN was designed to natively tackle security issues inherent to IP networks, it also introduces new security threats that may prevent its practical deployment by telco operators. Therefore, designing and implementing a dedicated security monitoring plane is essential to enable such future deployment. In this article, we present a set of contributions in this area. It first consists of featuring significant NDN attacks in a real operating context to evaluate their actual impact. Then, by analyzing the NFD data plane pipelines, we present a monitoring plane design that captures the state of NDN nodes by instrumenting 18 metrics with dedicated probes. We then correlate these metrics with a Bayesian network, which allows the detection of potential abnormal behaviors. To validate our approach, we demonstrate the efficiency of our monitoring plane in the detection of content poisoning attacks and interest flooding attacks in a testbed carrying real traffic.

International audience; The orchestration of countermeasures in the context of security incidents remains a challenging task for network operators. The main objective of this demonstration is to present how this orchestration is possible in the context of a virtualized NDN network. Based on an adaptation of the TOSCA topology and orchestration model, it is possible to trigger these countermeasures after the detection of NDN specific attacks. We show how the Montimage Monitoring Tool (MMT) has been adapted to detect typical Content Poisoning Attack (CPA), and how the orchestrator can trigger reactions to mitigate their impact on the network.