Latin-American Journal of Computing

Attack Taxonomy Methodology Applied to Web Services

Marcelo Invert Palma Salas — Mon, 08 Jan 2024 00:00:00 +0000

With the rapid evolution of attack techniques and attacker targets, companies and researchers question the applicability and effectiveness of security taxonomies. Although the attack taxonomies allow us to propose a classification scheme, they are easily rendered useless by the generation of new attacks. Due to its distributed and open nature, web services give rise to new security challenges. The purpose of this study is to apply a methodology for categorizing and updating attacks prior to the continuous creation and evolution of new attack schemes on web services. Also, in this research, we collected thirty-three (33) types of attacks classified into five (5) categories, such as brute force, spoofing, flooding, denial-of-services, and injection attacks, in order to obtain the state of the art of vulnerabilities against web services. Finally, the attack taxonomy is applied to a web service, modeling through attack trees. The use of this methodology allows us to prevent future attacks applied to many technologies, not only web services.

Pappus-Guldin theorems applied to the study of solid modeling with GeoGebra software

Luverci do Nascimento Ferreira, Fernanda Zastrow Tavares — Mon, 08 Jan 2024 00:00:00 +0000

In this work, we use Geogebra software to simulate the shape of objects (solids) in three dimensions from their photo and real dimensions using spline interpolation. With the reconstructed object, we analyze its volume and surface area using the Pappus-Guldin Theorems (PGT), the theorems that use mathematical analysis ideas to describe the volume and surface area by the sectional area and by the contour curve of the object. In the simulations, we tested the verification of the modeling for known solids (sphere and torus) and then analyzed some objects used in the industry, such as the packaging of products, pet bottles, yogurt containers, coffee powder packaging, aluminum soda cans, and the packaging of chocolate powder. We also analyzed some objects created by rotating bodies, such as the shape of a jar and an aluminum barrel, and also shapes found in nature, such as the shape of a pear and an egg. Modeling allows us to better understand the packaging used in the industry to minimize manufacturing costs and maximize its utility. Thus, we can modify these packages to obtain the best development of how these products are presented to the public, optimizing its format by analyzing its surface and its volume.

Numerical Modeling For Fracture Mechanics Problems Using The Open-source Fenics Platform

Mon, 08 Jan 2024 00:00:00 +0000

Fracture mechanics is the mechanical approach to fracture processes, which emerged due to limitations in the application of traditional concepts of Mechanics of Materials to predict the behavior of cracked materials. Analytical problem solutions with this approach may be unattainable, which allows the use of numerical modeling, such as the finite element method. However, the use of more advanced software that solves engineering problems numerically is limited by its high cost. FEniCS is an open source computational platform that solves partial differential equations by the finite element method. Thus, from a tutorial for this computational platform, this work proposes to reproduce a classic problem of linear elastic fracture mechanics, based on the validation of a comparison of a linear elastic problem with the commercial software ANSYS ®. With the help of the provided tutorial, an code was built to model a three-point bending test. Implemented with the aid of Gmsh and Paraview, it was possible to obtain satisfactory results, and to show that FeniCS is a powerful and accessible tool for solving fracture mechanics problems.

Single Phase Variable Reluctance Motor Design Using Particle Swarm Optimization

Mon, 08 Jan 2024 00:00:00 +0000

Electrical engines are built under electromagnetism concepts to create mechanical power, those can be seen as simple machines, as it depends on reluctance, even being called as “reluctance motor”, what gives this engine the possibility of being widely used for many purposes. The main objective of this research is to minimize copper losses of a single-phase 6x6 variable reluctance synchronous motor. For that, a Particle Swarm Optimization (PSO) algorithm will be used to obtain the optimum configuration through the Finite Elements Method (FEM). In this context, electric motor design equations were dimensioned based on similar machines. The next procedure was to use FEMM (Finite Element Method Magnetics) software, that allows the magnetic flow density analysis inside the motor air gap. Finally, it is noteworthy that the copper losses results were analyzed before and after the variable reluctance motor optimization with computational tools.

An approach for optimizing resource allocation and usage in cloud computing systems by predicting traffic flow

Sello Prince Sekwatlakwatla, Vusumuzi Malele — Mon, 08 Jan 2024 00:00:00 +0000

The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA's prediction of 79.8% and XGBoost's prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation.

Exploring Topics in Information Technology Open Educational Resources through the LDA Algorithm

Mon, 08 Jan 2024 00:00:00 +0000

This paper explores the application of machine learning and text mining techniques to discover OER issues in the context of Engineering Education. Applying the LDA (Latent Dirichlet Allocation) algorithm, themes are extracted from OER, it is possible to consider them as additional metadata. This augmentation serves to enhance the description and categorization of OER. Furthermore, this study introduces a methodology to automatically identify topics in open educational resources. In this research, a dataset of 80 OER was obtained from the Skills Commons repository. The highest coherence value achieved at 0.42, emerged when the number of topics was 9 in the LDA model. These nine topics are closely associated with Information Technology Education.

Alzheimer's diagnosis system based on magnetic resonance imaging using the VGG16 algorithm

Werner Shtaniklao Ucañay Barreto, Marco Antonio Coral Ygnacio — Mon, 08 Jan 2024 00:00:00 +0000

Early diagnosis of Alzheimer's disease is essential to provide timely treatment to patients. In this regard, a system for diagnosing Alzheimer's disease based on magnetic resonance imaging and utilizing a convolutional neural network algorithm called VGG16, has been developed. Magnetic resonance images of patients with and without Alzheimer's disease were collected and processed. These images were used to train the algorithm, which learned to identify and associate patterns with the disease. Subsequently, tests were performed with a set of unseen images to evaluate the diagnostic ability of the system. Through the analysis of magnetic resonance images, the VGG16 algorithm has shown a capacity of over 82% to correctly recognize these signs. These results validate the effectiveness of the artificial intelligence-based approach for diagnosing Alzheimer's disease.

Editorial

Denys A. Flores (LAJC) — Mon, 08 Jan 2024 00:00:00 +0000

The constant evolution of Computer Science challenges researchers to push the boundaries of innovation within a multidisciplinary landscape. From the Editorial of the Latin-American Journal of Computing, we are pleased to present to our readership this number, which showcases cutting-edge research in different applications of this field. The first article explores the application of Pappus-Guldin Theorems in solid modeling
using spline interpolation. Here, researchers demonstrate the potential of mathematical analysis in order to deliver more cost-effective computing-based solutions to possibly optimize industrial packaging design. Similarly, in the second article, numerical modeling is used to overcome the limitations of traditional approaches for analyzing linear elastic fracture mechanics by comparing the results obtained using commercial and open- source platforms.

Conversely, the work featured in the third article presents essentially non-oscillatory schemes for understanding the flow of two-phase fluids in oil extraction scenarios. Numerical methods are successfully employed to analyze mixing profiles of saturated water and petroleum fluids, demonstrating their importance for understanding fluid dynamics in porous materials. Likewise, the fourth article explores the usage of particle swarm optimization for enhancing the efficiency of a single-phase variable reluctance motor design. The authors demonstrate that minimizing copper losses is possible through finite element method analysis.

Addressing the evolving landscape of cybersecurity is the focus of the fifth article. The authors introduce a methodology for categorizing and updating attacks on web services, which contributes to a better understanding of vulnerabilities for preventing web-based attacks. In addition, the sixth article discusses the optimization of resource allocation on Cloud Computing by predicting traffic flow. The researchers employ machine learning models like ARIMA, Monte Carlo, and XGBoost for such predictive analysis.

Finally, the seventh and eight articles cover medical diagnosis and educational needs, respectively. In the former, an early-diagnosis method for Alzheimer's is featured using magnetic resonance imaging and the VGG16 Algorithm. The authors justify the effectiveness of employing AI to aid the diagnosis of such disease with a capacity
exceeding 82 per cent. In the latter, machine learning and text mining techniques are used to explore open educational resources (OER) for automatically identifying topics, enhancing their description and categorization.

In conclusion, the articles brought to you in this number provides a unique perspective to the different applications of Computer Science, and the dynamic nature of the research carried out in this contemporary discipline. Thanks to the authors who contributed to the ever-growing body of knowledge in this field, wishing them, and all our readers, a successful year 2024.

“Let science be the vessel to carry our dreams beyond the limits of our imagination”

Essentially non-oscillatory schemes applied to Buckley-Leverett equation with diffusive term

Raphael de Oliveira Garcia, Graciele Paraguaia Silveira — Mon, 08 Jan 2024 00:00:00 +0000

The purpose of this work was to investigate the flow of two-phase fluids via the Buckley-Leverett equation, corresponding to three types of scenarios applied in oil extraction, including a diffusive term. For this, a weighted essentially non-oscillatory scheme, a Runge-Kutta method and a central finite difference were computationally implemented. In addition, a numerical study related to the precision order and stability was performed. The use of these methods made it possible to obtain numerical solutions without oscillations and without excessive numerical dissipation, sufficient to assist in the understanding of the mixing profiles of saturated water and petroleum fluids, inside pipelines filled with porous material, in addition to allowing the investigation of the impact of adding the diffusive term in the original equation.