Currently, organizations face the need to create scalable applications in an agile way that impacts new forms of production and business organization. The traditional monolithic architecture no longer meets the needs of scalability and rapid development. The efficiency and optimization of human and technological resources prevail; this is why companies must adopt new technologies and business strategies. However, the implementation of microservices still encounters several challenges, such as the consumption of time and computational resources, scalability, orchestration, organization problems, and several further technical complications. Although there are procedures that facilitate the migration from a monolithic architecture to micro-services, none of them accurately quantifies performance differences. The current study aims primarily to analyze some related work that evaluated both architectures. Furthermore, we assess the performance and relationship between different variables of an application that runs in a monolithic structure compared to one of the micro-services. With this, the state-of-the-art review was initially conducted, which confirms the interest of the industry. Subsequently, two different scenarios were evaluated: the first one comprises a web application based on a monolithic architecture that operates on a virtual server with KVM, and the second one demonstrates the same web application based on a microservice architecture, but it runs in containers. Both situations were exposed to stress tests of similar characteristics and with the same hardware resources. For their validation, we applied the non-parametric regression mathematical model to explain the dependency relationship between the performance variables. The results provided a quantitative technical interpretation with precision and reliability, which can be applied to similar issues.

Phishing is a type of cyber-attack that aims to deceive users, usually using fraudulent web pages that appear legitimate. Currently, one of the most-common ways to detect these phishing pages according to their content is by entering words non-sequentially into Deep Learning (DL) algorithms, i.e., regardless of the order in which they have entered the algorithms. However, this approach causes the intrinsic richness of the relationship between words to be lost. In the field of cyber-security, the innovation of this study is to propose a model that detects phishing attacks based on the text of suspicious web pages and not on URL addresses, using Natural Language Processing (NLP) and DL algorithms. We used the Keras Embedding Layer with Global Vectors for Word Representation (GloVe) to exploit the web page content’s semantic and syntactic features. We first performed an analysis using NLP and Word Embedding, and then, these data were introduced into a DL algorithm. In addition, to assess which DL algorithm works best, we evaluated four alternative algorithms: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). As a result, it can be concluded that the proposed model is promising because the mean accuracy achieved by each of the four DL algorithms was at least 96.7%, while the best performer was BiGRU with 97.39%.

We have developed a low-cost wireless monitoring system, that enables air quality referential parameters measurements based on a multilayer distributed model with an Arduino platform. This is an Internet of Things application, of which a physical object is embedded with electronics, software, sensors and wireless connectivity to allow monitoring air pollution on real-time. Agile methodologies such as Scrum and Extreme Programming were used in order to ensure software quality. The electronic device is equipped with three sensors, which determines carbon monoxide (CO) as well as carbon dioxide (CO ₂ ) concentrations and powder density, using an API developed in C++ language. The validation of the mentioned concept has been realized in a variety of sites in Ecuador, namely in the cities of Quito, Amaguaña and Tena. The obtained results of air pollutants concentration are compared and conformable with the referential values established by international environment organizations like World Health Organization (WHO) and US EPA.