Selected Projects 2025
Internet Stability and Security
Regional mapping of malicious infrastructure and command and control servers in Latin America
Grants
This project seeks to identify, map and analyze active malicious infrastructure in Latin America, with a focus on command and control (C2) servers linked to malware and cyber threats. Through a combination of automated techniques and manual analysis using tools such as Shodan and Censys, indicators of compromise (IOCs), fingerprints and network data will be collected to allow these servers to be located in five key countries in the region. The objective is to obtain a technical vision of the Latin American threat landscape, identify trends by country, ASN or ISP, and better understand the attack surface in our networks. The project will generate a replicable methodology, a technical report of findings and an open workshop for threat laboratories and technical communities in the region, promoting local research and monitoring capabilities. All work will be developed by the ZoqueLabs team, who have experience in digital threats, OSINT and infrastructure analysis.
Evaluation of the implementation and impact of post-quantum cryptography algorithms on DNSSEC and TLS protocols
Grants
This project proposes to evaluate the feasibility, performance impact and best practices for the integration of post-quantum algorithms in the DNSSEC and TLS protocols, two fundamental pillars of Internet security. DNSSEC will be the main focus as it heavily employs public key cryptography for digitally signing DNS responses, making it an excellent environment to measure the practical effects of large-scale PQC adoption. The project will also investigate the use of post-quantum algorithms in the TLS handshake, which uses public key cryptography for key exchange and server authentication. The project methodology will combine computational simulations and the assembly of physical network prototypes, including Internet of Things (IoT) devices, to evaluate the impact of PQC in resource-constrained scenarios. Metrics such as latency, throughput, CPU, and memory consumption will be monitored to quantify the effects of adopting the new algorithms.
Internet Access and Connectivity
Internet Laboratory in Cartagena Rural
Award
The “Internet Laboratory in Cartagena Rural” is a pilot initiative of Aditum, ISA Impact (Impact innovation program of ISA Intercolombia S.A.) and Airband (Microsoft’s CSR program) with the purpose of closing the digital divide in Latin America, where more than 80 million people in rural areas lack access to fixed internet, drastically limiting their human development and in particular the possibilities of social mobility.
The “Internet Laboratory in Cartagena Rural” is our pilot in the community of Arroyo Grande, Cartagena de Indias, where we have managed to successfully connect more than 300 homes and various institutions, including 3 educational institutions, 4 health posts and nearly 30 businesses and enterprises. This deployment has allowed us to validate the technical and economic viability of our hypotheses, as well as their capacity to generate a tangible impact on the quality of life, education, economic opportunities and overcoming poverty in the benefited communities. Thanks to this, the internet laboratory in Cartagena Rural is positioned as a catalyst for sustainable and scalable digital inclusion in the region.
Open and Free Internet
Processing of Private Data for Use in Technological Research and Development
Grants
Several research and technological development projects are based on Artificial Intelligence (AI) techniques, statistical models, data mining, among others. These approaches require data to support the implementation of technological solutions. In this context, this project aims to develop a platform for making data available for research and technological development. Additionally, this solution will be integrated with a cloud environment, allowing greater capacity and availability of computational resources (storage and processing). Therefore, the project will have the following activities: (I) Systematic review of privacy laws and implementation of anonymization techniques; (II) Development of an API for data reception; (IV) Perform data anonymization; (V) Make anonymized data available on an open platform; and, (VI) Implement the solution in a cloud environment.
Artificial Intelligence applied to the Internet and Networks
RedLLM-Integrator: Strengthening SOCs through Responsible Integration of AI Agents and Capacity Transfer
Grants
The RedLLM-Integrator project proposes a solution to strengthen the capabilities of Security Operations Centers (SOCs) in Latin America through the progressive and ethical integration of explanatory artificial intelligence tools into their existing processes. Instead of training a new model from scratch – which implies high costs, technological dependence and advanced knowledge in AI infrastructure – the proposal focuses on leveraging already established architectures such as Retrieval-Augmented Generation (RAG) and pre-trained Large Language Models (LLMs) to support offensive simulation, identification of vulnerabilities and the generation of understandable recommendations in digital security environments.
COMCAIIAM: Combat Against Internet Threats Using Multiview Artificial Intelligence
Grants
In this project, Multiview AI algorithms will be explored and implemented that have the ability to generate levels of uncertainty in their predictions in such a way that it is possible to quantify how reliable a given prediction is. This way, if a prediction is known to be unreliable, an expert can be notified to make the final decision. Multiview AI algorithms with reliability quantification will be tested in different scenarios relevant to ISP, IXP, and IoT systems, including anomaly detection and device attacks. Specifically, we will seek to combine specific protocol characteristics, statistics based on flows, contents, and packet-level behaviors, with the objective of building more efficient and discriminative multiview representations for supervised learning tasks.