Conference Agenda

Distributed denial of service (DDoS) attacks constitute a critical threat to software-defined network (SDN) infrastructures, compromising operational availability due to centralization that makes the controller a single point of failure. This review aims to identify scientific evidence on the effectiveness of artificial intelligence-based strategies for detecting and mitigating DDoS attacks in corporate SDN infrastructures. Method: A systematic review was conducted using PICO methodology in SCOPUS, analyzing 248 bibliographic records until June 2025, applying specific criteria that resulted in 20 selected studies focused on AI strategies and critical corporate SDN infrastructures. Results: AI techniques demonstrated significant superiority achieving accuracies between 97% and 99.81% compared to 75% of traditional methods. Hybrid CNN-LSTM models with optimization algorithms reduced false positives from 15% to 0.24%-2% and improved processing speed by 35%. Four main approaches were identified: ML/DL models, adaptive precision, proactive detection, and false positive reduction. Conclusions: Artificial intelligence establishes a new paradigm in SDN cybersecurity, offering proactive, adaptive, and energy-efficient detection, although it requires greater validation in real production environments to confirm its complete operational applicability.

Manual invoice processing in small service companies often leads to delays, human errors, and inefficient administrative workflows. This study presents the design and implementation of an intelligent chatbot integrated with WhatsApp, which aims to automate the generation of electronic invoices through voice commands. The system combines AI-based natural language processing, low-code automation (n8n), and official validation services, such as the SUNAT API, to extract, validate, and structure billing data. Real-world testing at Servicios Generales Alvarado Hns demonstrated a significant reduction in processing time (from 9 to 10 minutes to less than 2 minutes per invoice) and the elimination of billing errors. User satisfaction reached an average score of 4.65, validating the system's usability and effectiveness. The results confirm that AI-based mobile solutions can improve administrative efficiency in micro and small businesses, especially in mobile operating environments.

This article presents an experimental proposal for the production of eco-friendly bricks made from plastic waste, specifically polyethylene terephthalate (PET) and polypropylene (PP), as a sustainable alternative to conventional construction materials. The main objective was to evaluate the technical and environmental feasibility of a small-scale prototype suitable for non-load-bearing enclosures and interior applications. Several tests were carried out using mixtures of PET, PP, sawdust, and synthetic glue, revealing limitations in cohesion and durability in the initial formulations.

The optimal combination was achieved using only recycled plastics. The study demonstrates the potential for plastic waste valorization through simple processes, promoting circular economy principles and generating environmental, social, and economic benefits. This approach is replicable in urban contexts with high volumes of plastic waste and can be integrated into community initiatives or sustainable construction projects.

Data transport networks are vital strategic infrastructures that enable interconnection between countries, governments, and populations. In Australia, the National Broadband Network (NBN) has expanded digital service access to remote areas by combining fiber optics, satellite, and wireless technologies. Similarly, Peru developed the National Fiber Optic Backbone Network (RDNFO) to bridge the digital divide and provide internet access to rural and vulnerable communities. However, the Peruvian Army has limited connectivity to this network, relying in part on the private operator NEXTNET, which involves high installation and maintenance costs. To address this challenge, this study presents a tool that estimates distances between Army units and RDNFO nodes, aiming to optimize fiber optic network design and deployment. Developed using Google Apps Script—a JavaScript-based environment that automates tasks and integrates geospatial data through Google Maps API—the tool improves planning efficiency. Applied to the Army’s III Division (III-DE), it estimated distances with an average error of 12.41%, adding 23.446 kilometers and an extra cost of USD 18,756.80. Despite this added cost, the tool offers enhanced accuracy and reliability, reducing potential deployment risks and supporting more strategic decisions in extending military connectivity through the national fiber optic infrastructure.

Food and Nutritional Security (FNS) is a strategic pillar for sustainable development and the realization of the human right to adequate food. Around the world, FNS observatories and monitoring platforms have emerged as key tools for tracking the availability, access, consumption, and biological use of food, as well as food safety. However, their digital portals often exhibit critical limitations in functionality, usability, data openness, and frequency of updates. This study aimed to design and validated an evaluation instrument to assess the functionality of FNS observatory web portals using global methodological benchmarks. A quantitative, descriptive, and analytical approach was applied, based on the content analysis of 50 portals worldwide. The proposed instrument includes eight sections aligned with FNS pillars and transversal criteria such as digital accessibility, data update frequency, interactivity, and open data availability. Validation was conducted through expert judgment, and results were systematized using descriptive statistics. The findings underscore the relevance of standardized evaluation tools to strengthen data governance, enhance institutional transparency, and support the continuous improvement of digital food information systems globally.