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Clearing Clouds from the Horizon: Latency Characterization of Public Cloud Service Platforms
Get PDFServices rely more and more on cloud platforms to
offer their products to end users. This implies that being able
to estimate the latency required to reach those cloud platforms
is of growing importance. To shed light on this crucial aspect,
we perform a three-month measurement campaign involving
traceroute measurements every 30 minutes over 256 pairs of
source-destination probes, where the vantage points are located
in different Cloud Service Providers (CSPs) and the destination
probes belong to one of the main Infrastructure Operators (IO s) of
Spain. We provide interesting insights obtained from analyzing the
data resulting from this campaign. Among them, we observe that,
as expected, distance is the unavoidable factor impacting cloud
latency. Yet, other results are less anticipated, such as the great
stability of the network, or the lack of performance difference
when comparing standard and premium network service tiers.
We also analyze the potential of forecasting the cloud latency
both for future samples but also for unobserved connections.Spanish Ministry of Economic Affairs and Digital TransformationEuropean Union NextGeneration-EUTRUEinpres
How Mature is 5G Deployment? A Cross-Sectional, Year-Long Study of 5G Uplink Performance
Get PDFAfter a rapid deployment worldwide over the past few years, 5G is expected to have reached a mature deployment stage to provide measurable improvement of network performance and user experience over its predecessors. In this study, we aim to assess 5G deployment maturity via three conditions: (1) Does 5G performance remain stable over a long time span? (2) Does 5G provide better performance than its predecessor LTE? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting a cross-sectional, year-long measurement study of 5G uplink performance. Leveraging a custom Android App, we collected 5G uplink performance measurements (of critical importance to latency-critical apps) spanning 8 major cities in 7
countries and two different continents. Our measurements show that 5G deployment in major cities appears to have matured, with no major performance improvements observed over a one-year period, but 5G does not provide consistent, superior measurable performance over LTE, especially in terms of latency, and further there exists clear uneven 5G performance across the 8 cities. Our study suggests that, while 5G deployment appears to have stagnated, it is short of delivering its promised performance and
user experience gain over its predecessorMinisterio de Ciencia e InnovaciónTRUEpu
IoC Stalker: Early detection of Indicators of Compromise
Get PDFOnline underground forums are used by cybercriminals to share information and knowledge related to malicious activities. Participants exchange "Indicators of Compromise" (IoCs) within the discussions. These may include Hashes, Domains, URLs, or IPs with potential malicious intent. While Open Source Intelligence (OSINT) eventually identifies these malicious IoCs, it may take an extensive amount of time, sometimes up to years, before they are identified as threats. However, the context in which these IoCs appear, and the information provided through the posts' and authors' context can already offer valuable insights about their malicious nature. Unfortunately, the large amount of unstructured noisy forum data presents a hurdle for automation.
In this paper, we address the challenge of automatically distinguishing between posts containing IoCs posing a threat and those being harmless. We design a learning pipeline that does not use features derived from IoCs, enabling a timely identification of novel threats. We operate over a temporal representation of forum data and offer valuable insights into the optimal time window that tracks concept drift. We also study which types of IoCs are harder to predict (e.g., IPs) and how transfer learning from other types can help to improve their identification. We conduct our analysis on a prominent hacking forum, spanning over 18 years of data, and find that our model can detect IoCs ≈490 days before they appear in OSINT.TRUEpu
Implementación y evaluación de setchain
Get PDFLa escalabilidad de los blockchains es un desafío que limita su adopción. Por ello se están desarrollando diversas técnicas para mejorar su rendimiento. Setchain es un tipo de datos concurrente y distribuido que mejora la escalabilidad frente a un blockchain, al relajar el requisito de orden total entre las transacciones. Setchain implementa conjuntos distribuidos que solo crecen (grow-only sets) cuyos elementos (por ejemplo, transacciones) no están ordenados. En un setchain se pueden imponer barreras de sincronización para crear épocas. Cada época contiene un subconjunto, acordados por consenso, de los últimos elementos agregados. Aunque los elementos dentro de una época no están ordenados, los elementos entre diferentes épocas sí están ordenados según los números de época. Este tipo de ``blockchain relajado'' es adecuada para aplicaciones que no requieren un orden total entre las transacciones dentro del mismo bloque.
En una edición anterior de las JCSD , se presentaron tres algoritmos diferentes de setchain. En esta presentación comparamos el rendimiento y la escalabilidad de estos tres algoritmos: (a) Vanilla, una implementación básica; (b) Compresschain, que agrupa un conjunto de transacciones en un lote y comprime el lote antes de transmitirlo como una transacción; y (c) Hashchain, donde se emplea una función hash para convertir un lote de transacciones en un hash de longitud fija, facilitando así su transmisión. Para este tercer algoritmo, también proponemos una técnica de inversión de hash para recuperar el lote de transacciones original a partir del hash para su verificación. Tanto Compresschain como Hashchain utilizan un software intermedio llamado Collector, que agrega transacciones de clientes y genera los lotes.
Los tres algoritmos de Setchain han sido implementados en la plataforma de aplicación blockchain CometBFT dentro de un entorno Docker, donde cada nodo Docker opera un servidor y un cliente de CometBFT. Las implementaciones se han evaluado en escenarios que involucran cuatro, siete y diez nodos servidores de CometBFT, enfocándose en parámetros como el tamaño máximo del bloque y la tasa de envío. Los resultados demuestran que los algoritmos de setchain mejoran significativamente la escalabilidad de blockchain.FALSEinpres
Unveiling the 5G Mid-Band Landscape: From Network Deployment to Performance and Application QoE
Get PDF5G in mid-bands has become the dominant deployment of choice in the world. We present - to the best of our knowledge - the first comprehensive and comparative cross-country measurement study of commercial mid-band 5G deployments in Europe and the U.S., filling a gap in the existing 5G measurement studies. We unveil the key 5G mid-band channels and configuration parameters used by various operators in these countries, and identify the major factors that impact the observed 5G performance both from the network (physical layer) perspective as well as the application perspective. We characterize and compare 5G mid-band throughput and latency performance by dissecting the 5G configurations, lower-layer parameters as well as deployment settings. By cross-correlating 5G parameters with the application decision process, we demonstrate how 5G parameters affect application QoE metrics and suggest a simple approach for QoE enhancement. Our study sheds light on how to better configure and optimize 5G mid-band networks, and provides guidance to users and application developers on operator choices and application QoE tuning. We released the datasets and artifacts at https://github.com/SIGCOMM24-5GinMidBands/artifacts.Ministerio de Ciencia e InnovaciónMinisterio de UniversidadesTRUEpu
Towards Data-Driven Management of Mobile Networks through User Plane Inference
Get PDFGrowing network complexity has rendered human-in-the-loop network management approaches obsolete. The advent of Software-Defined Networking (SDN) has enabled network automation, with Machine Learning (ML) models running in the control plane. However, such control plane models do not run at line rate and would not satisfy the stringent latency requirements of time-sensitive next-generation applications. In this PhD project, we exploit recent advances in programmable switches and associated languages like P4 to enable data-driven management of networks by running ML models for inference in programmable switches at line rate, with high throughput and low latency. Resulting contributions include solutions for in-switch classification at packet level, flow level, or both, with use cases in network security, service identification, and device fingerprinting in commercial off-the-shelf switches.European Union’s Horizon Europe research and innovation programme under Marie Skłodowska-Curie grant agreement no. 860239Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101139270TRUEpu
Sustainable Provision of URLLC Services for V2N: Analysis and Optimal Configuration
Get PDFThe rising popularity of Vehicle-to-Network (V2N) applications is driven by the Ultra-Reliable Low-Latency Communications (URLLC) service offered by 5G. The availability of distributed resources could be leveraged to handle the enormous traffic arising from these applications, but introduces complexity in deciding where to steer traffic under the stringent delay requirements of URLLC. In this paper, we introduce the V2N Computation Offloading and CPU Activation (V2N-COCA) problem, which aims at finding the computation offloading and the edge/cloud CPU activation decisions that minimize the operational costs, both monetary and energetic, under stringent latency constraints. Some challenges are the proven non-monotonicity of the objective function w.r.t. offloading decisions, and the no-existence of closed-formulas for the sojourn time of tasks. We present a provably tight approximation for the latter, and we design BiQui, a provably asymptotically optimal and with linear computational complexity w.r.t. computing resources algorithm for the V2N-COCA problem. We assess BiQui over real-world vehicular traffic traces, performing a sensitivity analysis and a stress-test. Results show that BiQui significantly outperforms state-of-the-art solutions, achieving optimal performance (found through exhaustive searches) in most of the scenarios.L.E. Chatzieleftheriou is a Juan de la Cierva awardee (JDC2022-050266-I), funded by MCIU/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR, and MADQuantum-CM project, funded by the Regional Government of Madrid and the EU “NextGenerationEU”/PRTR. This work was supported by the Remote Driver project (TSI-065100-2022-003) funded by Spanish Min- istry of Economic Affairs and Digital Transformation, and is also partially supported by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D SORUS project.TRUEpu
A Synthetic Data Generation System based on the Variational-Autoencoder Technique and the Linked Data Paradigm
Get PDFCurrently, the generation of synthetic data has become very fashionable, either due to the need to create data in certain specific contexts or to study unknown scenarios among other reasons. Additionally, synthetic data is a critical component in training machine learning models in the presence of little data. This work proposes a Synthetic Data Generation System (SDGS) architecture to allow synthetic data generation to be fully automated. SDGS is based on the Variational AutoEncoders (VAE) learning technique, and has three main capabilities. The first is related to the ability to extract data samples from multiple sources using the Linked Data (LD) paradigm. The second is linked to the ability to merge data sets to increase the amount of information that can be provided to the VAE-based synthetic data generator. The last one is related to having a Feature Engineering layer to create new features by generating or extracting information from the dataset and then selecting the features that provide the best information for the VAE model. A case study is described in detail to show the new functionalities of the SDGS, such as dataset extraction from different sources using LD, dataset merging using pivots, and the application of different feature engineering methods. Finally, two metrics are used to evaluate the quality of the generated datasets in different case studies. The first one is the accuracy to analyze the performance of the models generated with the new SDGS functionalities, obtaining results above 90%. The second one is the two-Sample Hotelling's T-Squared Test to determine the quality of the synthetic data generated by the system, obtaining synthetic datasets very similar to the original datasets.TRUEpu
Precision farming using autonomous data analysis cycles for integrated cotton managemen
Get PDFPrecision farming (PF) allows the efficient use of resources such as water, and fertilizers, among others; as well, it helps to analyze the behavior of insect pests, in order to increase production and decrease the cost of crop management. This paper introduces an innovative approach to integrated cotton management, involving the implementation of an Autonomous Cycle of Data Analysis Tasks (ACODAT). The proposed autonomous cycle is composed of a classification task of the population of pests (boll weevil) (based on eXtreme Gradient Boosting-XGBoost), a diagnosis-prediction task of cotton yield (based on a fuzzy system), and a prescription task of strategies for the adequate management of the crop (based on genetic algorithms). The proposed system can evaluate several variables according to the conditions of the crop, and recommend the best strategy for increasing the cotton yield. In particular, the classification task has an accuracy of 88%, the diagnosis/prediction task obtained an accuracy of 98 %, and the genetic algorithm recommends the best strategy for the context analyzed. Focused on integrated cotton management, our system offers flexibility and adaptability, which facilitates the incorporation of new tasks.TRUEpu
Demystifying Privacy in 5G Stand Alone Networks
Get PDFEnsuring user privacy remains critical in mobile networks, particularly with the rise of connected devices and denser 5G infrastructure. Privacy concerns have persisted across 2G, 3G, and 4G/LTE networks. Recognizing these concerns, the 3rd Generation Partnership Project (3GPP) has made privacy enhancements in 5G Release 15. However, the extent of operator adoption remains unclear, especially as most networks operate in 5G Non Stand Alone (NSA) mode, relying on 4G Core Networks. This study provides the first qualitative and experimental comparison between 5G NSA and Stand Alone (SA) in real operator networks, focusing on privacy enhancements addressing top eight pre-5G attacks based on recent academic literature. Additionally, it evaluates the privacy levels of OpenAirInterface (OAI), a leading open-source software for 5G, against real network deployments for the same attacks. The analysis reveals two new 5G privacy vulnerabilities, underscoring the need for further research and stricter
standards.TRUEinpres