Companion Proceedings of the Brazilian Symposium on Computer Networks and Distributed Systems (SBRC)
O Workshop de Trabalhos de Iniciação Científica e de Graduação (WTG-SBRC), evento do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), com periodicidade anual, visando promover a participação de estudantes de graduação e de recém-graduados na produção e divulgação de trabalhos científicos sobre temas relacionados às áreas de redes de computadores e sistemas distribuídos.
Sociedade Brasileira de Computação - SBC
Mininet-WPAN: Enabling WPAN and IoT Emulations with 802.15.4 Support
The rapid growth of the Internet of Things (IoT) has created a significant demand for reliable and scalable emulation platforms capable of supporting low-power, wireless networks. In this context, IEEE 802.15.4 has emerged as a key protocol for enabling Wireless Personal Area Networks (WPANs) in IoT applications. This paper introduces Mininet-WPAN, an extension of the Mininet-WiFi emulator designed to support IEEE 802.15.4-based networks through the integration of the mac802154 Linux wireless device driver. Mininet-WPAN enables emulation of WPANs, allowing researchers and developers to explore and optimize various network configurations and protocols, including the Routing Protocol for Low-Power and Lossy Networks (RPL). We present three use cases demonstrating Mininet-WPAN’s ability to emulate RPL in both storing and non-storing modes, showcasing its flexibility for IoT research. By offering lightweight virtualization, Software-Defined Networking (SDN) integration, and customizable network interfaces, Mininet-WPAN provides a powerful tool for advancing the development of IoT networks. This work also contributes to the open-source community by making the platform freely available for further research and innovation in low-power wireless communication.
A New WAVE: Exploring New Load Pattern Models for Experimentation in Computer Networks
Experimentation is a crucial step in many types of scientific research, enabling researchers to evaluate the validity of their hypotheses. In computer networks, one of the key challenges during the experimentation phase is finding load generators capable of accurately modeling diverse traffic patterns for various applications. To address this issue, our previous work introduced a load generator designed to generate load based on real application behavior. In this sense, this work improves the WAVE Workload Assay for Verified Experiments. A new WAVE version can generate loads for three distinct patterns: sinusoid, flashcrowd, and step. Additionally, it now supports microbursts and container-based environments.
Horus-CDS: The Digital Eye on Smart Grid Security
This work proposes the Hórus-Cyber Detection for Smart Grids (Hórus-CDS), a tool based on temporal neural networks for detecting anomalies in the network traffic of a Smart Grid (SG). The model employs Temporal Convolutional Networks (TCNs), which leverage dilated causal convolutions to capture sequential patterns and enhance real-time inference. Unlike conventional approaches, the tool enables greater parallelization, lower latency, and an interface for monitoring and decision-making. The tool was validated in two scenarios: a simulated SG environment and a real-world deployment at Neoenergia, where its resilience against DDoS attacks was tested. The results demonstrated that Hórus-CDS outperformed other approaches, ensuring the operational continuity of power distribution systems.
A Robust Software Sensor to Identify People in a Video Scene
The development of smart cities and the Internet of Things paradigm allows the creation of more efficient, sustainable and safe cities. Among the innovations, smart cameras stand out for identifying objects and people, enabling continuous monitoring and real-time data collection for different environments. This article presents a smart software sensor for counting people in video scenes, developed for Computer Sciences Lab. and integrated with the LCC-IoT application. It allows monitoring and generating alarms in specific situations, such as the presence of people outside of permitted hours, while preserving users’ privacy. The sensor was implemented to maintain continuous and concurrent operation across multiple cameras, controlling data acquisition and consistent generation of telemetry through semaphores, in addition to recording errors, making it robust. The solution uses the OpenCV and YOLO for image processing, MQTT and HTTP application protocols for sending telemetry.
Cyber Threat Intelligence for Enhanced Attack Detection and Incident Response
Detecting cyberattacks is a complex task, as it requires the analysis of a large volume of data. In this scenario, automating parts of the process is essential. Cyber Threat Intelligence consists of collecting, processing, and analyzing data related to attacks, where network traffic acts as a key source of information. The developed software constructs attack patterns based on past incidents and evaluates the match between these patterns and network logs, aiming to identify potential attackers and the techniques employed. The tests were conducted using artificial logs that simulate an attack, and the results indicate that the approach has potential, although refinements are needed for effective application in real-world scenarios.
THexCAD: A Prototyping and Simulation Platform for Area Coverage with Multiple Drones
This paper presents the THexCAD, a distributed platform for performing area coverage missions with multiple drones. The goal of our platform is to facilitate the testing, analysis, and validation of area coverage strategies in simulated and real environments. The main challenge in developing this type of application with multiple drones is the coordination of the drone swarm to cover an area efficiently, considering the movement, coordination, generation and allocation of subareas, and planning of paths for coverage. In this sense, THexCAD provides a framework that abstracts complex processes, allowing developers or users to focus on specific actions related to the mission with multiple drones. Initial experimental results show that THexCAD presents scalability when the number of drones involved in the mission varies.
HackInSDN Dashboard: web platform for experimenting in computer networking and cybersecurity through Kubernetes clusters
One way to address the quantity and complexity of cybersecurity incidents, which impact technological resources and sectors of society, is to invest in user awareness and advanced training for professionals with practical exercises and laboratories. Tools that simplify access to experimental environments with specialized networking and cybersecurity resources are essential in this context. This paper presents the HackInSDN Dashboard, a web platform that facilitates experimentation in networking and cybersecurity by orchestrating testbed resources. The Dashboard can be deployed to orchestrate resources from a Kubernetes cluster, enabling its use in cloud commercial environments and scientific testbeds, especially the Brazilian Research and Education Network (RNP) testbed. The tool has been used experimentally in undergraduate and graduate courses and proof-of-concept labs in the RNP’s Hackers do Bem project.
ns3-oran-customizable-db: A Tool for Building a Customizable Database for Open RAN Simulations
An essential part of developing applications for the control and diagnosis of a Radio Access Network (RAN) is simulation. It is crucial to evaluate proposed solutions in a simulated scenario that closely resembles reality to avoid potential failures in implementation. The objective of this work is to add functionalities to the ”ns3-oran”extension of the ”ns-3”network simulator. The implemented functionalities give users the freedom to use the extension without restrictions on what data can be sent to the Near-RT RIC. The ability to collect, store, and analyze various types of data is fundamental in the development and implementation of machine learning-based solutions. Thus, the developed tools enable the creation of simulation scenarios capable of training reinforcement learning models, using performance metrics that were previously impossible to manipulate within the simulator.
Lab Cyber Academy: A Cyber Range for Information Security Training Using AWS and IaC
Cybersecurity is essential due to the rapid evolution of cyber threats, which jeopardize the integrity of data and systems. Cyber Range are controlled virtual environments that allow the simulation of attacks (such as DDoS and vulnerability exploits) and defenses (such as firewalls and traffic analysis), promoting hands-on learning of security strategies. The Information Security discipline is fundamental in Information and Communication Technology courses, but the lack of free Cyber Ranges and resources in institutions hinders proper training. This work proposes the Lab Cyber Academy, a free Cyber Range solution that uses Containernet and Docker to create emulated attack and defense scenarios, implemented in the AWS Academy cloud and based on infrastructure as code. Experiments and evaluations with students from UFC prove its effectiveness, validating its feasibility for cybersecurity training.
NFV-Prime: A Platform for Prototyping and Visualization of Virtualized Network Functions
O paradigma NFV (Network Functions Virtualization) vem se popularizando nas redes modernas. Este trabalho apresenta a ferramenta NFV-Prime que permite a prototipação de VNFs (Virtual Network Functions) de forma simples e intuitiva. A ferramenta integra quatro componentes em um pipeline: Editor, Gerenciador de Interfaces Virtuais de Rede, Gerador de Tráfego e Visualização de Resultados. A NFV-Prime permite a implementação de VNFs em Python 3, além de oferecer uma ampla variedade de parametrizações e configurações das interfaces de rede e dos tráfegos gerados durante a execução da VNF. Para demonstrar a ferramenta, é apresentada a criação de um balanceador de tráfego stateful.
UNetyEmu: Unity-based simulator for aerial and non-aerial vehicles with integrated network emulation
This work presents UNetyEmu as a novel framework that enables realistic multi-vehicle experiments with aerial and non-aerial mobility alongside network emulation. By integrating Unity’s 3D simulation and Mininet-WiFi’s real-time network emulation, UNetyEmu allows researchers to conduct studies in smart city applications like 5G vehicular communication, edge computing, and drone coordination. Our framework is easily customizable and facilitates the evaluation of drone-related algorithms, such as obstacle avoidance and route planning, and studying network aspects, such as coverage and RSSI.
Mininet-GUI: A Visual and Interactive Approach for Experimentation in SDN Networks
Mininet is a widely adopted emulator for prototyping and experimenting with Software-Defined Networking (SDN). Its main graphical interface, MiniEdit, however, offers limited functionality and low interactivity, which hampers adoption and constrains advanced experimentation. Although several alternative tools have been proposed, none has become a definitive replacement, largely due to the absence of essential integrated features. We present Mininet-GUI, a web-based tool that enables real-time creation, editing, and execution of Mininet topologies. Mininet-GUI allows dynamic manipulation of hosts, switches, controllers, and links, as well as direct command execution through the interface. Unlike existing approaches, it provides interactive topology editing, simplified terminal access via WebShell, and support for exporting and importing topologies in JSON and Python formats. Mininet-GUI is designed to make SDN experimentation more accessible and efficient for both beginners and advanced researchers. This paper describes its architecture, capabilities, and applicability through common use cases.
AGATA - Architecture for Automatic Management of Federated Learning Tasks
O aprendizado federado protege a privacidade ao efetuar o treinamento do modelo nos dispositivos dos usuários. Entretanto, o operador em nuvem não possui acesso aos dispositivos, que estão muitas vezes indisponíveis, para experimentar com diferentes modelos e hiperparâmetros. Este trabalho propõe uma ferramenta que resolve esses problemas ao oferecer funcionalidades para a inicialização, finalização e troca automática de múltiplas tarefas de aprendizado federado. A implementação da AGATA é compatível com ferramentas populares, como o Flower, e se diferencia de outros sistemas por automatizar a troca de hiperparâmetros e códigos.
Cloud AutoDroid: A Scalable Distributed System for Running Generative AI Tools
The Cloud AutoDroid was developed to solve scalability problems in the execution of experiments with complex neural networks, such as MalSynGen, which demand high computational power. With a distributed architecture, the tool allows for the autoscaling execution of AI tasks. Available as a SaaS (Software as a Service), the Cloud AutoDroid offers a platform for large-scale experimentation, although it still requires intervention for the initial allocation of nodes.
VulnSyncAI: NLP and LLMs for Construction and Continuous Updating of Vulnerability Datasets Fontes e conteúdo relacionado
The construction and maintenance of up-to-date vulnerability datasets face challenges such as lack of standardization and the need for automation. In this work, we present VulnSyncAI, a modular tool that uses NLP (Natural Language Processing) and LLMs (Large Language Models) to correlate information from multiple sources, ensuring updated and relevant datasets. VulnSyncAI enhances the effectiveness of AI models in threat detection by automating processes and increasing efficiency in creating representative datasets.
TrueState-SNA: Uma Solução Baseada em SSoT para Automação de Redes Heterogêneas
A TrueState-SNA, baseada em uma arquitetura de camadas e software de código aberto, emerge como uma solução inovadora para o gerenciamento de redes heterogêneas, combinando flexibilidade e eficiência. Ao integrar Netbox, Net2d e Ansible, a solução centraliza a gestão de configurações em uma Single Source of Truth (SSoT), automatizando a transição de estados desejados para estados operacionais. As principais contribuições da TrueState-SNA incluem: uma arquitetura aberta e adaptável, suporte a múltiplos fabricantes e tecnologias, redução do risco de lock-in e a promoção de uma gestão de rede consistente e confiável.
Artificial Intelligence as a Service Architecture: an innovative approach for Computer Vision applications
In recent years, Artificial Intelligence (AI) has grown significantly across various domains such as transportation, healthcare, and security. However, current implementations of intelligent services face challenges in enhancing the personalized and large-scale use of AI. This work presents Artificial Intelligence as a Service (AIaaS), an innovative approach to effectively manage the lifecycle of diverse AI models and paradigms, while offering them as a service for heterogeneous devices, multiple users, and modern applications. We explored the feasibility of delivering AI resources in different network architectures, such as edge computing and mobile networks, which provide a flexible and scalable environment that allows users to acquire cognitive services in the AI life cycle. Also, our approach facilitates personalized and scalable AI solutions, fostering innovation and expediting the deployment of intelligent applications across diverse contexts, making it suitable for real-world scenarios. Ph.D. Thesis defended on September 23, 2024 (FACOM – UFU). Committee: Prof. André R. Backes (UFSCar), Prof. Bruno A. N. Travençolo (UFU), Prof. Alessandra A. Paulino (UFU), Prof. João Henrique S. Pereira (UFU), Prof. Dalcimar Casanova (UTFPR), and Prof. João B. Florindo (UNICAMP). Full text available at: doi.org/10.14393/ufu.te.2024.675.
SARIK: A proposed Framework for enhancing security in Kubernetes through network policies
The increasing adoption of Kubernetes as a container orchestration platform brings benefits to the management of distributed applications but also poses security challenges, especially concerning the control of traffic between components. This dissertation presents the SARIK framework (Automatic Security of Iptables Rules in Kubernetes), a framework that automates network policies to enhance the security of Kubernetes clusters. The methodology includes the integration of SARIK into kube-proxy to dynamically apply blocking rules and traffic control. In a test environment with Minikube, Prometheus, and Grafana, metrics such as latency, response rate, and throughput were evaluated across different traffic scenarios. The results indicate that SARIK improves security by reducing network vulnerabilities while keeping the cluster’s performance practically unchanged. The analysis indicates that SARIK represents an advancement in automated security for Kubernetes, balancing protection and operational efficiency, with the potential for future large-scale adaptations.
Urban Perception Extraction from Texts Shared on Social Media: Framework and Applications
This thesis presents an automatic, generic framework for extracting urban perceptions from Location-Based Social Network (LBSN) data. The framework is organized into five key layers: Data Collection, Preprocessing and Embeddings, Model Training, Knowledge Extraction, and Applications. By leveraging deep learning techniques, including advanced sentence embedding methods, the framework captures both lexical and semantic nuances in textual data, thereby efficiently extracting user perceptions of urban environments. This approach eliminates the need for labor-intensive field surveys and manual data extraction, allowing scalable real-time analysis. We validated the framework by applying it to selected urban areas in Chicago, New York City, and London, demonstrating its effectiveness in uncovering valuable insights about urban perceptions. Furthermore, a comparative evaluation using a public dataset derived from volunteers’ perceptions in a controlled experiment revealed a high level of agreement between the two sets of results. As a proof-of-concept, we introduce Real-Estate Urban Perceptions (REAL-UP), an innovative tool designed to enhance the real estate marketplace. REAL-UP provides interactive 2D maps that integrate traditional real-estate data (e.g., rent prices and property types) with enriched information on neighborhood emotions, sentiments, and brief narrative reviews generated by a Large Language Model (LLM) based on LBSN messages.
Scaling Stateful Network Services on Multicore Architectures
This thesis investigates the effective scheduling of TCP stacks alongside applications on multicore architectures, focusing on the trade-offs in allocating workers for both TCP and application processing. It explores the interplay between stateful network protocols with strong guarantees and the challenges of scheduling such protocols alongside multicore applications. To allow fair comparisons, we design and implement Demieagle, a benchmark framework that allows the execution of “apples-to-apples” experiments to uncover the trade-offs of different multicore scheduling policies and architectures. We also address the complexity of scaling stateful network functions, which require per-packet state updates. During a scaling operation, workers need to synchronize access to a shared state to avoid race conditions and to guarantee that network functions process packets in arrival order. Unfortunately, the classic approach to control concurrent access to a shared state with locks does not scale to today’s throughput and latency requirements. To address these challenges, we design, implement, and evaluate Dyssect, a system that enables dynamic scaling of stateful network functions by disaggregating their states. Dyssect’s state disaggregation allows the offloading of stateful network functions to programmable NICs and makes it easier to explore hardware-software trade-offs that better suit specific network functions and traffic loads. Our experimental evaluation shows that Dyssect reduces tail latency up to 32.04% and increases throughput up to 19.36% compared to state-of-the-art competing solutions.
Automatic Inference of BGP Community Semantics
The Border Gateway Protocol (BGP) enables communication between Autonomous Systems (ASes) on the Internet. BGP offers significant flexibility for traffic engineering through BGP communities, which are operatordefined tags that convey information or requests in route announcements. Unfortunately, the absence of standardized semantics or centralized repositories for BGP communities complicates and limits their use, hindering the effective management of interdomain routing. This thesis develops techniques to infer BGP community semantics using public BGP data from routing collectors, overcoming the lack of documentation and providing datasets that can be automatically updated. We first propose a set of techniques to infer location communities, which are communities related to entities or locations traversed by a route. We apply our techniques to billions of routing records from public BGP collectors and show that they produce high precision (ranging from 86% to 93%) and recall (ranging from 72% to 81%). We also design and evaluate algorithms to automatically uncover BGP action communities and ASes that violate standard practices, revealing undocumented relationships between them (e.g., sibling relationships). Our experimental evaluation uncovers previously unknown AS relationships and shows that our algorithm to identify action communities achieves average precision and recall of 92.5% and 86.5%, respectively.
F-NIDS Sistema de Detecção de Intrusão Baseado em Aprendizado Federado
In this dissertation, we address the challenges of scalability and security faced by Network Intrusion Detection Systems (NIDS) in IoT scenarios, particularly those arising from privacy concerns. To overcome these challenges, we propose the F-NIDS (Federated Network Intrusion Detection System), a system designed based on Federated Learning (FL), integrating asynchronous communication techniques and Differential Privacy (DP). Additionally, three strategies were developed to balance performance and privacy. The results demonstrate that F-NIDS achieves high robustness and performance, with superior detection values in distributed scenarios. The parameter adjustment strategy allowed the identification of the optimal level of Gaussian noise, ensuring privacy preservation without compromising accuracy. Therefore, F-NIDS represents a significant advancement in the state of the art, combining scalability, security, and privacy in distributed systems, reaffirming its effectiveness and potential to redefine intrusion detection standards in IoT environments.
Enhancing Cybersecurity of Automotive Ethernet Networks with Deep Learning-based Intrusion Detection Systems
The growing number of attack surfaces in vehicles, driven by increased connectivity and the demand for automotive Ethernet, a high-bandwidth in-vehicle network technology, emphasizes the need for effective security mechanisms. This dissertation proposes two deep learning-based intrusion detection systems (IDSs) for identifying cyberattacks in automotive Ethernet networks. The first proposal features an IDS based on a multi-criteria optimized convolutional neural network, designed to enhance detection accuracy, speed, and storage efficiency simultaneously. The second proposal introduces a multi-stage deep learning-based IDS, where the initial stage prioritizes fast detection while the second stage focuses on achieving more accurate results and classifying attacks. The main results of this dissertation comprehend the publication of a paper in SBRC 2023 and another in the Ad Hoc Networks journal.
Adaptive Network Management in 6G O-RAN: A Framework for Dynamic User Demands
6G networks demand unparalleled adaptability and energy efficiency. This work presents a dynamic O-RAN-based framework integrating the SMO, Near-RT RIC, and Non-RT RIC for real-time analytics, intelligent policy application, and adaptive energy management. By deploying rApps (e.g., Energy Savings) and xApps, the framework dynamically clusters and manages radio nodes, optimizing resource allocation while reducing power consumption. A prototype implementation shows substantial improvements over traditional static approaches, notably conserving energy during low-demand periods while sustaining high performance at peak times. Key components enable seamless integration across the network, demonstrating the framework’s capability to adapt to varying demands and establishing a new benchmark for 6G network management.
Carbono 21: Promoting Forestation Through Digital Assets
Despite its environmental benefits, forestation is often overlooked in favor of more profitable crops with negative environmental impacts. In response, this work promotes forestation through tokenization, creating financial incentives for landowners who plant trees. Forested areas and green assets are transformed into Non-Fungible Tokens (NFTs) on a consortium blockchain. Their metadata is automatically updated via satellite-based deforestation tracking systems, something unseen in the literature. The solution also contributes to distributed systems and the environment by proposing several transparency mechanisms – often lacking in forestation efforts and permissioned blockchains –, enabling authorities to audit the transaction and utilization of green assets.
Low-cost animal and pedestrian crossing detection in rural roads using WiFi sensing and deep learning
Road traffic accidents involving animals cause great health, environmental, and monetary costs every year, specially on rural areas. Current animal detection systems suffer from either cost, scalability or accuracy issues, which prevent their effective use in a more extensive manner. In our research, we explore WiFi sensing to monitor events in rural roads, using low-cost IoT devices to collect WiFi data, publishing the first open CSI dataset of animal crossings and applying machine learning techniques to detect and classify them. Event detection runs on ESP32-S3 devices, while classification runs on Raspeberry Pi 4 devices, both with accuracy of at least 95%. Our system enables a scalable and cost-effective solution for monitoring multiple kilometers of roads.
Dynamic Network Slicing for User Mobility Support in 5G Networks
Network slicing has been a key supporting technology for resource management in modern computer networks, from 5G to 6G and beyond. This paper summarises the thesis developed by the first author at the IC/UNICAMP. This work presents new resource management mechanisms for the dynamic network slicing allocation problem to support mobile users in 5G/edge computing infrastructures. A network simulator was developed to evaluate the proposed solutions. The simulator is available as free software and has been adopted as the validation environment by the scientific community. The results of this work indicate the positive impact of slices’ dynamic resource allocation in dealing with demand variations triggered by mobile users.
Container Migration Performance Evaluation: An Approach Based on Stochastic Petri Nets
Containerization has emerged as a transformative technology in modern data centers, enabling efficient resource management and improving operational flexibility across various applications. While often associated with platforms like Docker and Kubernetes, container-based solutions are widely integrated into cloud environments such as AWS, Microsoft Azure, and Google Cloud. In large-scale distributed systems, efficient container migration is crucial to manage server downtime, consolidate resources, and ensure reliability in mobile edge computing scenarios. The problem with evaluating container migration performance lies in the high cost and computational demand of real-world experiments. Assessing different migration strategies efficiently remains a challenge, particularly for stateful containers, which require structured modeling approaches to quantify their impact on system performance. The proposal of this study is to develop Stochastic Petri Net (SPN) models to assess container migration strategies. The approach includes two models—one incorporating an absorbing state and another without—analyzing key migration techniques: Cold, PreCopy, PostCopy, and Hybrid. These models evaluate critical performance metrics, including Migration Total Time (MTT), Mean Migration Time (MMT), utilization, discard probability, and migration rate. Furthermore, a sensitivity analysis based on the Design of Experiments (DoE) was conducted for the Hybrid migration strategy to identify key performance factors. The conclusion of this research is that by providing an analytical framework for container migration evaluation, it enhances the understanding of migration performance dynamics and supports decision-making in cloud and edge computing infrastructures.
Strategies and Analyses for Efficient Resource Allocation in Smart Cities Using Edge and Cloud Computing
The resource allocation problem in Smart Cities, integrating Edge and Cloud Computing resources, is addressed in this article. Three models based on Integer Linear Programming are proposed, aiming to: construct feasible solutions, minimize costs, and reduce the makespan. An analysis is presented in which a percentage increase in costs is allowed in exchange for a lower makespan. Experiments using exact algorithms were conducted. When considering the evolution of the network from 2G to 6G, it is observed that the rejection rate tends to increase due to reduced coverage. In cost minimization, still within the context of 6G technologies, costs can be reduced by up to ≈ 4.38% compared to the model that only generates a feasible solution. When optimizing the makespan, the execution time increased from 0.03s to up to 2.35s, but with a reduction of ≈ 84.7% in the makespan.
Less is More: Evaluating the Impact of Model Compression on Federated Learning Efficiency
The growing number of devices connected to the Internet has generated a large volume of data, increasingly driving the use of Artificial Intelligence (AI). However, centralized learning approaches raise concerns regarding user privacy. Federated Learning (FL) emerges as a distributed alternative, as it avoids sharing users’ raw data and complies with privacy regulations. Nevertheless, frequent communication between devices and the server in FL leads to high bandwidth and energy consumption. In networks with limited resources, solutions to mitigate this problem are crucial. In this context, considering the possibility of reducing traffic in FL, we investigate different model compression techniques, aiming to balance model quality and communication cost. The results indicate that compression techniques effectively reduce the volume of transmitted data without degrading model performance, even in scenarios involving imbalanced data.
On the Usage of Grammar-based Fuzzing to Evaluate the Implementation of AMQP Brokers
Automated testing strategies, like fuzzing, can be used to ensure the security and reliability of software systems, and those that implement communication protocols have specific needs. One class of protocols that deserves to be highlighted, both for its expansion in use and for its complexity, is the publishsubscribe (pub-sub) class. Among several pub-sub protocols, AMQP does not receive much attention from the literature regarding fuzzing testing. This paper fills this gap by proposing the usage of grammar-based fuzzing to test AMQP brokers. The proposal is integrated into a new free and open-source fuzzer called AMQPGRAM. Experiments with AMQPGRAM attest to its capacity to cover 100% of the AMQP messages related to the establishment of connections.
Task Offloading from UAVs to Vehicles: An Analysis of Mobility Patterns in Region Monitoring
This study analyzes the efficiency of offloading tasks from Unmanned Aerial Vehicles (UAVs) to ground vehicles in monitoring missions, considering static, circular, and square mobility patterns. A tool was developed to generate UAV traffic scenarios, and an offloading algorithm was adapted. Results show that, despite up to a 7% nominal variation in failure rates for circular mobility, no statistically significant differences are observed among the patterns, nor a relevant impact on offloading time, especially for larger tasks. The findings highlight the potential to optimize UAV mobility for greater computational efficiency, with applications in smart cities, including traffic monitoring, accident detection, and stolen-vehicle tracking, reinforcing the need for further research.
Evaluation of strategies for improving anomaly detection in DNS traffic
The increasing complexity of cyber threats makes the detection of anomalies in DNS traffic crucial for ensuring network security. Although several studies have been conducted to explore this process, the presence of false positives remains a significant challenge in the analysis. This work aims to validate DNS anomaly detection techniques with low false positive rates, using data from threat intelligence sources and domain analysis techniques applied in similar studies. The validation was performed using datasets publicly available and widely used. Despite the fact that the results did not meet the expectations, they indicate the need to evaluate the data using other techniques, and these aspects can be explored in future works.
An Modular Architecture for IoT Environment Monitoring
The use of Internet of Things (IoT) devices for monitoring individuals and environments has become common in several fields such as industry, agriculture, and healthcare. This paper proposes a modular, generalist and multi-scenario architecture for monitoring environments with IoT devices. To validate the proposed architecture, we developed a proof of concept that demonstrated the feasibility of implementing and using the architecture in a real environment.
Online Adequation of Network Traffic Traces at Clients to Feed Intrusion Detection Systems
The constant emergence of new applications and zero-day attacks make public datasets for training IDSs quickly obsolete, justifying that organizations (re-)generate their private datasets frequently. This paper presents a proposal to capture traffic on devices with low computational power and adapt it to the expected format for training IDSs without storing the raw traffic. Experiments have shown that the proposal reduces the execution time by up to 76% in generating datasets, despite the possibility of packet loss in the captured traffic depending on the bit rate. As an additional contribution, the software that implements the proposal is available as free and open-source software.
Intrusion Detection System based on Federated Reinforcement Learning
In this project, the goal is to apply Federated Reinforcement Learning (FRL) techniques to the context of intrusion detection in Internet of Things (IoT) scenarios. Various essential IoT systems share sensitive information that needs to be transmitted securely and privately. Therefore, it is crucial to monitor these systems to prevent and defend against attacks that aim to expose their contents or weaken them. In this regard, we propose an anomaly detection FRL model, which is capable of efficiently assessing and detecting attacks, without sharing local data in the process.
On the Balance between Performance and Fairness in Multi-Tenant Cache Environments
Multi-tenant web caches are susceptible to performance interference, where one tenant’s activity can negatively impact others. Traditional fairness policies, like FairShare, aim to mitigate this by providing fair resource allocation. However, their suitability for caching environments is not well established. In this work, we investigate the performance of FairShare using production traces from a large-scale e-commerce platform. Through simulation, we evaluate its effectiveness in balancing fairness and overall system performance. Our results reveal that while FairShare eliminates interference, it significantly degrades the cache Hit Ratio, demonstrating that classical fairness policies are not directly applicable to caching scenarios. This highlights the need for cache-aware fairness mechanisms that consider the specific characteristics of web caches.
Distributed Monitoring of Photovoltaic Plants Using LoRa Wireless Sensor Networks with Mesh Topology
This study employs a LoRa wireless sensor network to monitor operational parameters in a photovoltaic power plant. Adopting a mesh topology, the network collects data on fuse status and temperature in stringboxes, transmitting them to a central station. The tests demonstrate reliable collection, even in a challenging environment with solar panel movement throughout the day. The results validate LoRa mesh networks as a robust alternative for continuous monitoring of photovoltaic power plants.
The Cloud Central Committee: A Case Study on Capacity Planning in Large IT Enterprises
Large companies often decentralize into sub-organizations to accelerate decision-making. For instance, when using IaaS, these sub-organizations might independently purchase cloud resources. While flexible, such a decentralized approach can be more expensive than a company-wide, centralized strategy, due to a wider variation in usage patterns of sub-organizations. To investigate these trade-offs within the scenario of a major IaaS consumer, we analyzed multiple years of cloud purchase data from a partner company to quantify potential cost savings under different organizational models. Our findings show that centralization yielded significant savings, potentially reducing IaaS costs by nearly 9%, translating into millions of dollars in savings in this case study.
Exploring Network Slicing in 5G Networks in an Educational Context
The use of digital tools in education has accelerated during the pandemics, highlighting tools such as Moodle and intelligent systems for personalized teaching. Smart Education is increasingly empowered by the 5G networks tailored for educational purposes, which provide high data transfer rates, reduced latency, and reliable connectivity, crucial for handling multimedia applications. In this context, this paper presents a proof-of-concept based on network slicing to optimize resources in a Distance Learning environment using the Moodle platform. A xApp is used to monitor the Radio Access Network (RAN) and enable dynamic load balancing, based on the profiles of connected users.
Spatio-Temporal Vehicle Re-Identification for Intelligent Traffic Systems
Most computer vision systems implemented in the context of urban monitoring rely on metrics obtained from isolated cameras, which limits large-scale analysis capabilities. This work proposes a modular and lightweight system capable of operating on embedded devices in a decentralized manner, enabling the generation of new correlated data across multiple monitoring points. With this approach, it becomes possible to obtain metrics such as travel times and most-used routes, contributing to improved traffic management and strategic urban planning.
Evaluating the Limits of Wi-Fi 5, and Wi-Fi 6 Networks for Industrial Video Applications
This study evaluates the performance of Wi-Fi 5 and Wi-Fi 6 wireless networks, comparing them to a wired network. The analyzed metrics include throughput and packet loss. The tests were conducted in a controlled environment with 13 clients connected to an AP (Access Point), transmitting data corresponding to a video stream at varying resolutions. The results show that Wi-Fi 6 offers better performance, especially in scenarios with multiple devices, while Wi-Fi 5 presents limitations at higher resolutions. The wired network consistently demonstrated the highest stability, with limitations in higher resolution transmissions.
Evaluating Semantic Caching in Practice: A Study on a LLM-Driven Distributed Application in a Brazilian EdTech
Large Language Models (LLMs) support various business functions, such as Alura’s use of GPT-4 to assess students’ answers. However, high computational and financial costs, along with response time issues, limit scalability. While caching mechanisms offer an alternative, traditional cache fails to evaluate queries semantically, leading to low hit rates. This work evaluates semantic caching on an Alura’s dataset of 94, 913 answers from 20, 639 students. Results show that 45.1% of LLM requests could be served from the cache, significantly reducing costs and improving response times by 4–12× for cache hits.
SkyLedger: Testbed for Airspace Management in Intelligent Transportation Systems
The versatility of drones, combined with their low infrastructure requirements for operation, has driven expectations of a sharp increase in airspace demand. In this context, the evolution of air traffic management models becomes essential to support this growing demand while ensuring safety, efficiency, and scalability for Advanced Air Mobility (AAM). This work presents requirements, challenges, and opportunities for a testbed based on permissioned Distributed Ledger Technologies (DLT) to enable data interoperability in AAM environments in compliance with Brazilian regulatory frameworks.
RotaWise: Intelligent Platform for Urban Maintenance
This paper presents RotaWise, an intelligent platform for urban maintenance that integrates cameras and an edge device within a vehicle for real-time video processing and urban issue identification. The system leverages computer vision and AI technologies to monitor roads and sidewalks for problems such as potholes, trash, and debris. The experimental results demonstrated RotaWise’s ability to process up to three cameras simultaneously, offering realtime event detection and metadata transmission.