IMDEA Networks Institute Digital Repository
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Service for Deploying Digital Twins of QKD Networks
Get PDFQuantum technologies promise major advances in different areas. From computation to sensing or telecommunications, quantum implementations could bring significant improvements to these fields, arousing the interest of researchers, companies, and governments. In particular, the deployment of Quantum Key Distribution (QKD) networks, which enable the secure dissemination of cryptographic keys to remote application entities following Quantum Mechanics Principles, appears to be one of the most attractive and relevant use cases. Quantum devices and equipment are still in a development phase, making their availability low and their price high, hindering the deployment of physical QKD networks and, therefore, the research and experimentation activities related to this field. In this context, this paper focuses on providing research stakeholders with an open- access testbed where it is feasible to emulate the deployment of QKD networks, thus enabling the execution of experiments and trials, where even potential network attacks can be analyzed, without the quantum physical equipment requirement, nor compromising the integrity of an already built QKD network. The designed solution allows users to automatically deploy, configure, and run a digital twin environment of a QKD network, offering cost-effectiveness and great flexibility in the study of the integration of quantum communications in the current network infrastructures. This solution is aligned with the European Telecommunications Standard Institute (ETSI) standardized application interface for QKD, and is built upon open-source technologies. The feasibility of this solution has been validated throughout several functional trials carried out in the 5G Telefónica Open Network Innovation Centre (5TONIC), verifying the service performance in terms of speed and discarded qubits when generating the quantum keys.TRUEpu
Towards Real-Time Intrusion Detection in P4-Programmable 5G User Plane Functions
Get PDFRecent works have shown that Machine Learning (ML) models can be deployed in P4-programmable user planes for line rate inference on live traffic and that these user planes can also be used to accelerate the 5G User Plane Function (UPF). This work builds on these capabilities to explore how ML inference in the user plane can facilitate real-time intrusion detection in 5G networks. As a proof-of-concept, we describe how an ML model could be deployed into the UPF as a special Packet Detection Rule (PDR). We then train and deploy a tree-based classifier into a P4-programmable switch acting as the UPF and conduct experiments on a testbed with off-the-shelf hardware using experimental data from a 5G test network on a university campus. Our results confirm that running ML-based intrusion detection on P4-based UPFs ensures line-rate attack detection and classification with an accuracy of up to 98% in terms of F1 score, while keeping switch resource consumption increase under control.Project PCI2022-133013 (ECOMOME), funded by MICIU/AEI/10.13039/501100011033 and the European Union "NextGenerationEU"/PRTRSmart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101139270NetSense grants no. 2023-5A/TIC-28944 funded by Comunidad de MadridTRUEinpres
Det-RAN: Data-Driven Cross-Layer Real-Time Attack Detection in 5G Open RANs
Get PDFFifth generation (5G) and beyond cellular networks are vulnerable to security threats, primarily due to the lack of integrity protection in the Radio Resource Control (RRC) layer. In order to address this problem, we propose a real- time anomaly detection framework that leverages the concept of distributed applications in 5G Open RAN networks. Specifically, we identify Physical Layer (PHY) features that can generate a reliable fingerprint, infer in a novel way the time of arrival of uplink packets lacking integrity protection, and handle cross- layer features. By identifying legitimate message sources and detecting suspicious activities through an Artificial Intelligence (AI) design, we demonstrate that Open RAN-based applications that run at the edge can be designed to provide additional security to the network. Our solution is first validated in extensive emulation environments achieving over 85% accuracy in predicting potential attacks on unseen test scenarios. We then integrate our approach into a real-world prototype with a large channel emulator to assess its real-time performance and costs. Our solution meets the low-latency real-time constraints of 2 ms, making it well-suited for real-world deployments.Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEUU.S. National Science FoundationAir Force Office of Scientific ResearchOffice of Naval ResearchTRUEinpres
JUMP: Joint communication and sensing with Unsynchronized transceivers Made Practical
Get PDFWideband millimeter-wave communication systems can be extended to provide radar-like sensing capabilities on top of data communication, in a cost-effective manner. However, the development of joint communication and sensing technology is hindered by practical challenges, such as occlusions to the line-of-sight path and clock asynchrony between devices. The latter introduces time-varying timing and frequency offsets that prevent the estimation of sensing parameters and, in turn, the use of standard signal processing solutions. Existing approaches cannot be applied to commonly used phased-array receivers, as they build on stringent assumptions about the multipath environment, and are computationally complex. We present JUMP, the first system enabling practical bistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions. Our system compensates for the timing offset by exploiting the channel correlation across subsequent packets. Further, it tracks multipath reflections and eliminates frequency offsets by observing the phase of a dynamically-selected static reference path. JUMP has been implemented on a 60 GHz experimental platform, performing extensive evaluations of human motion sensing, including non-line-of-sight scenarios. In our results, JUMP attains comparable tracking performance to a full-duplex monostatic system and similar micro-Doppler quality with respect to a phase-locked bistatic receiver.TRUEpu
Learning to Learn How to Manage Network Resources with Loss Function Meta-Learning
Get PDFThe evolution of communication networks towards self-configuring systems requires the development of anticipatory approaches for network management to realize the envisioned concept of a zero-touch network orchestration. Current anticipatory network intelligence solutions rely on a well-defined loss function, which means that they require perfect knowledge of the relationship between the proactive management decisions and the consequent system performance. However, in anticipatory networking, there exist many tasks where characterizing such a relationship in advance is not possible. In such tasks, it is possible to measure the resulting performance of a management decision a posteriori, but we cannot know a priori the resulting performance of a certain management decision. A simple example of such tasks could be the maximization of the monetary profit when allocating resources to end users: when taking a certain resource allocation decision, it is possible to measure the profit afterwards, but it would be extremely difficult to determine a priori the resulting monetary profit. To close this gap, we present a novel two-fold learning approach, which is able to jointly learn the relationship between the prediction and the target management objective at the same time as it apprehends to anticipate the corresponding task. This method lays the foundations to the automated adaptation of network intelligence to specific complex objectives in zero-touch network management. We apply this method to different use cases of interest including monetary profit maximization.Comunidad de Madrid - Atracción de talentoEuropean UnionTRUEpu
Ultra-Low Latency User-Plane Cyberattack Detection in SDN-based Smart Grids
Get PDFModern power grids are smart, comprising millions of electronic devices interconnected by communication networks. This exposes them to a wide range of cyberattacks which could lead to power outages and data breaches with far-reaching consequences. Thus, the timely detection of such attacks is essential. Machine Learning (ML) models are widely used for cyberattack detection in Smart Grids (SG) based on Software-Defined Networks (SDN). However, these models either run in external servers or in-network, fully in the application or control plane or distributed between the control and user planes. In all three cases, the models do not run at line rate and incur hundreds of milliseconds of delay in attack detection. This paper explores how ML inference in programmable switches can enable accelerated attack detection and mitigation in SGs at line rate with sub-microsecond delay. The proposed workflow brings the concept of user plane inference to SDN-based SGs and deploys a trained Decision Tree (DT) model into the switch pipeline for real-time inference on live traffic. The model is implemented in a testbed with production-grade Intel Tofino switches, where experiments are run with a DNP3 intrusion detection dataset. Results reveal how the model can distinguish multiple attacks against SGs with an accuracy of 99%, incurring a delay within 356 nanoseconds, while consuming a tiny portion of the available resources in the switch.Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101139270Project PCI2022-133013 (ECOMOME), funded by MICIU/AEI/10.13039/501100011033 and the European Union "NextGenerationEU"/PRTRTRUEpu
Gaming on the Edge: Performance Issues of Distributed Online Gaming
Get PDFWe study the performance of online games played over a platform that implements gaming as a service (GaaS) in a mobile network slice that hosts concatenated virtual network functions (VNFs) at the edge. The distributed gaming architecture is based on edge computing facilities, whose utilization must be
carefully planned and managed, so as to satisfy the stringent performance requirements of game applications. The game manager must consider the latency between players and edge server VNFs,
the capacity and load of edge servers, and the latency between edge servers used by interacting players. This calls for a careful choice about the allocation of players to edge server VNFs, aiming
at extremely low latency in interactions resulting from player’s commands. We develop an analytical model, which we validate with experiments in the wild, and show that, under several combinations of system parameters, deploying gaming VNFs at the edge can deliver better performance with respect to cloud
gaming, in spite of the complexities arising from the distribution of gaming VNFs over edge servers. Our analytical model provides a useful tool for edge gaming systems performance prediction, thus supporting the management of GaaS applications.Best paper award runner-upTRUEpu
A semi-supervised learning algorithm for multi-label classification and multi-assignment clustering problems based on a Multivariate Data Analysis
No full textClassically, in classification and clustering problems, an individual is exclusively assigned to a class (labeled data) or a cluster (unlabeled data). However, the assignment to a single class or cluster may be too strict in several real-world domains. In many cases, an individual may belong to multiple classes or clusters at the same time. On the other hand, we are starting to see machine learning algorithms that solve the problem of multi-label classification and multiple cluster assignment, but there are no algorithms that solve both problems simultaneously. Classic semi-supervised algorithms can work with labeled and unlabeled data simultaneously, but these types of algorithms assign individuals to a single class or a single cluster. Today, artificial intelligence faces the challenge of developing semi-supervised learning algorithms to work with semi-labeled data that can be assigned to different classes or clusters at the same time. In particular, this paper proposes a semi-supervised learning algorithm to fill this gap, which can solve the problems of multi-label classification and multiple cluster assignment simultaneously, on a semi-labeled dataset. This proposal is based on the LAMDA algorithm (Learning Algorithm for Multivariate Data Analysis), which calculates the degree of membership of a data to a group/class. In this work, a membership threshold is defined, which allows individuals to be assigned to classes or clusters that have a membership greater than this membership threshold. Thus, the main contribution of this work is the development of a semi-supervised algorithm that can process semi-labeled datasets to assign them to multiple classes and/or clusters. Furthermore, the work defines a metric to evaluate its efficiency in a semi-supervised context, called Multi Label-Cluster Index (MULCI). This proposal is tested on several datasets from the domains of multi-label classification or multi-assignment clustering, or a combination of both, showing very encouraging results. Very good quality metrics results are achieved in multiclass and multicluster tasks.TRUEpu
Optimal allocation of tasks to networked computing facilities
Get PDFDistributed allocation of computing tasks over network resources is meant to decrease the cost of centralized allocation. However, existing analytical models consider practically indistinguishable re-
sources, e.g., located in the data center. With the rise of edge computing, it becomes important to account for the impact of diverse latency values imposed by edge/cloud data center locations. In this paper, we study the optimization of computing task allocation considering both the delays to reach edge/cloud data centers and the response times of servers. We explicitly evaluate the resulting performance under different scenarios. We show, through numerical analysis and real experiments, that differences in delays to reach data center locations cannot be neglected. We also study the price of anarchy of a distributed implementation of the computing task allocation and unveil important properties such as the price of anarchy being generally small, except when the system is overloaded, and its maximum can be computed with low complexity.TRUEpu
Uncovering Latent Patterns in Service-Level Spatiotemporal Mobile Traffic
Get PDFAs people increasingly rely on mobile applications for their daily activities and needs, the digital traces left by smartphones have become an essential instrument to provide information about human activities.
In this work, we focus on identifying latent structures in mobile traffic by targeting the traffic generated by individual applications, whose correlation with the urban environment has yet to be explored. Therefore, our mobile traffic analysis considers a third application-related dimension in addition to traditional spatial and temporal ones.
In this context, we show that tensor decomposition techniques can be applied to service-level mobile traffic and improve the identification and interpretation of patterns that may remain undiscovered if conventional approaches are adopted.Comunidad de MadridComunidad de MadridSpanish Ministry of Digital Transformation and Public Service and the European Union-NextGenerationEU/PRTRTRUEinpres