Selected Projects 2026
Internet Stability and Security
Development of an Intelligent, Low-Latency Open Wi-Fi Access Device
Grants
The project aims to develop an open-source software architecture to integrate L4S technology into Wi-Fi routers and GPON terminals, with the goal of reducing latency in wireless access networks. The solution will incorporate intelligent congestion management mechanisms based on Reinforcement Learning, capable of dynamically adapting to variations in Wi-Fi links. As a result, a Proof of Concept will be developed and validated, together with OpenWRT-compatible modules, seeking to provide Internet service providers with an open alternative that requires low computational resources and avoids vendor lock-in.
Validating BGP Filters with Active BGP Advertisement Manipulation
Grants
BGP filters are essential for preventing route leaks and prefix hijacks, but misconfigured or outdated filters can unintentionally block legitimate route propagation and are often difficult to detect until a network disruption occurs. Because BGP policies are largely opaque, these errors may remain hidden while traffic continues to reach its destination through alternative paths. This project will develop techniques that allow network operators to actively test and validate BGP filters by manipulating route advertisements and controlling how prefixes propagate through upstream networks. Using mechanisms such as selective advertisements, BGP communities, and BGP poisoning, the research will seek to identify incorrect filtering not only at direct providers but also several AS hops away. The techniques will be evaluated in real-world scenarios through the PEERING Testbed, and the resulting tool will be packaged for use by network operators in their own environments. The project will also seek collaboration with South American operators, IXPs, and research institutions to expand PEERING’s presence and improve visibility of networks across the LACNIC region.
PRTR: Secure and Post-Quantum-Ready BGP Routing on Open and Affordable Hardware for ISPs in the LACNIC Region
Grants
BGP routing security remains a challenge for Internet service providers, particularly those with more limited technical and financial resources. Across the LACNIC region, adoption of mechanisms such as RPKI/ROV and ASPA remains uneven, while the transition toward post-quantum cryptographic technologies introduces new challenges for network infrastructure. PRTR seeks to help reduce these barriers through an open and accessible platform that enables routing security mechanisms to be tested, validated, and deployed in real-world environments. The project will combine BGP validation testing, assessments of post-quantum technologies, and operator-oriented documentation, with a particular focus on the needs of small and medium-sized ISPs in Latin America and the Caribbean.
Between Law and Protocol: Measuring collateral impact and improving the visibility of Internet blocking in Brazil and Latin America
Grants
The growing number of orders to block websites and applications is creating new challenges for Internet stability in Latin America. When such measures are implemented directly at the IP address level, they may unintentionally affect numerous legitimate services that share the same infrastructure. This research seeks to measure and understand this collateral impact through observations carried out across different Internet service providers, identifying how blocking measures are implemented, where they occur, and what effects they have on other content and services. Based on this evidence, the project aims to improve visibility into these interventions and provide technical information that can contribute to dialogue between operators and authorities, supporting more precise measures with less impact on Internet stability and resilience across the region.
Detection of DDoS and ransomware attacks in real-world enterprise networks and servers using artificial intelligence models
Grants
Ransomware and DDoS attacks are among the leading threats to digital security worldwide and are becoming increasingly prevalent in Latin America, where organizations and service providers also face limitations in accessing advanced protection mechanisms. The research focuses on the use of Artificial Intelligence to improve the detection of these types of attacks, combining Machine Learning, Deep Learning, and generative AI techniques. The work will also use real traffic data and seek to validate the results in environments directly connected to industry. The initiative brings together academic and industry capabilities from Mexico and Brazil, with the goal of applying research results in real-world scenarios and helping reduce disruptions, financial losses, and risks associated with DDoS and ransomware attacks.
BGP-TI LAC: an automated regional observatory for detecting, classifying, and notifying BGP route hijacks in Latin America and the Caribbean, promoting RPKI adoption
Grants
BGP route hijacks continue to pose a risk to the security and stability of the Internet, while the adoption of mechanisms such as RPKI remains incomplete across Latin America and the Caribbean. At present, the region lacks a system capable of monitoring these incidents from a regional perspective, proactively notifying affected networks, and identifying recurring patterns. BGP-TI LAC proposes an automated regional observatory that uses public data sources to detect and classify anomalous BGP announcements in near real time, incorporating information on ownership, RPKI validation, topology, and historical behavior. The system also provides for notifications to affected networks and the corresponding CSIRTs, together with technical evidence and guidance to strengthen protection through RPKI. The project seeks to turn this evidence into an open, automated, and reusable tool that contributes to improving regional visibility and strengthening routing security across Latin America and the Caribbean.
Artificial Intelligence applied to the Internet and Networks
OpenAI-EdgeRouter: Routers with Federated Learning
Grants
The project aims to develop a decentralized, open-source ecosystem to optimize and protect Wi-Fi networks through Federated Learning techniques implemented directly on low-cost commercial routers running OpenWrt. Each router will be able to learn locally from network behavior without sharing users’ sensitive data, while contributing to the development of a global model that can improve overall network performance, energy efficiency, and security. The solution includes use cases such as optimizing energy consumption and Quality of Experience (QoE), as well as the autonomous detection and mitigation of DoS/DDoS attacks at the edge. Validation is being carried out on university Wi-Fi infrastructures, with the goal of developing accessible technology that can be applied in schools, community centers, and community networks across Latin America.
Autonomous intent-based WAN management using a local Large Action Model for Latin American operators
Grants
WAN operators in Latin America and the Caribbean face challenges related to budget constraints, limited availability of specialized technical staff, and dependence on commercial solutions to automate network management. In this context, the use of cloud-based language models can also introduce recurring costs, reliance on external connectivity, and risks related to data sovereignty. This research explores the use of a locally deployed Large Action Model to interpret natural language instructions and translate them into concrete actions on WAN infrastructure. The work will be carried out on a reproducible experimentation platform and will evaluate different levels of complexity, ranging from diagnostics and configuration adjustments to traffic engineering and incident response.