The Internet of Things (IoT) presents great potential in various fields such as home automation, healthcare, and industry, among others, but its infrastructure, the use of open source code, and lack of software updates make it vulnerable to cyberattacks that can compromise access to data and services, thus making it an attractive target for hackers. The complexity of cyberattacks has increased, posing a greater threat to public and private organizations. This study evaluated the performance of deep learning models for classifying cybersecurity attacks in IoT networks, using the CICIoT2023 dataset. Three architectures based on DNN, LSTM, and CNN were compared, highlighting their differences in layers and activation functions. The results show that the CNN architecture outperformed the others in accuracy and computational efficiency, with an accuracy rate of 99.10% for multiclass classification and 99.40% for binary classification. The importance of data standardization and proper hyperparameter selection is emphasized. These results demonstrate that the CNN-based model emerges as a promising option for detecting cyber threats in IoT environments, supporting the relevance of deep learning in IoT network security.

The early and accurate detection of Distributed Denial of Service (DDoS) attacks is a fundamental area of research to safeguard the integrity and functionality of organizations’ digital ecosystems. Despite the growing importance of neural networks in recent years, the use of classical techniques remains relevant due to their interpretability, speed, resource efficiency, and satisfactory performance. This article presents the results of a comparative analysis of six machine learning techniques, namely, Random Forest (RF), Decision Tree (DT), AdaBoost (ADA), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), and Dense Neural Network (DNN), for classifying DDoS attacks. The CICDDoS2019 dataset was used, which underwent data preprocessing to remove outliers, and 22 features were selected using the Pearson correlation coefficient. The RF classifier achieved the best accuracy rate (99.97%), outperforming other classifiers and even previously published neural network-based techniques. These findings underscore the feasibility and effectiveness of machine learning algorithms in the field of DDoS attack detection, reaffirming their relevance as a valuable tool in advanced cyber defense.

Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contrasting it with SMOTE and ADASYN. By injecting controlled perturbations into the minority class, our approach mitigates overfitting risks inherent in interpolation-based techniques. Five classifiers, including XGBoost and a convolutional neural network (CNN), were evaluated on augmented datasets. XGBoost achieved superior performance with Gaussian noise-augmented data (accuracy: 0.999507, AUC: 0.999506), outperforming SMOTE and ADASYN. These results underscore Gaussian noise’s efficacy in enhancing fraud detection accuracy, offering a robust alternative to conventional oversampling methods. Our findings emphasize the pivotal role of augmentation strategies in optimizing classifier performance for imbalanced financial data.

Neural networks have become a fundamental tool for solving complex problems, from image processing and speech recognition to time series prediction and large-scale data classification. However, traditional neural architectures suffer from interpretability problems due to their opaque representations and lack of explicit interaction between linear and nonlinear transformations. To address these limitations, Kolmogorov–Arnold Networks (KAN) have emerged as a mathematically grounded approach capable of efficiently representing complex nonlinear functions. Based on the principles established by Kolmogorov and Arnold, KAN offer an alternative to traditional architectures, mitigating issues such as overfitting and lack of interpretability. Despite their solid theoretical basis, practical implementations of KAN face challenges, such as optimal function selection and computational efficiency. This paper provides a systematic review that goes beyond previous surveys by consolidating the diverse structural variants of KAN (e.g., Wavelet-KAN, Rational-KAN, MonoKAN, Physics-KAN, Linear Spline KAN, and Orthogonal Polynomial KAN) into a unified framework. In addition, we emphasize their mathematical foundations, compare their advantages and limitations, and discuss their applicability across domains. From this review, three main conclusions can be drawn: (i) spline-based KAN remain the most widely used due to their stability and simplicity, (ii) rational and wavelet-based variants provide greater expressivity but introduce numerical challenges, and (iii) emerging approaches such as Physics-KAN and automatic basis selection open promising directions for scalability and interpretability. These insights provide a benchmark for future research and practical implementations of KAN.

Credit card fraud detection is a growing challenge in the financial domain due to data imbalance, where fraudulent transactions are minimal compared to legitimate ones. This study presents SynKGen, a data augmentation method using Kernel PCA with Gaussian perturbations to generate synthetic samples of the minority class, contrasting it with ADASYN and SMOTE. By introducing variance analysis with controlled perturbations in the minority class, the proposed approach mitigates the risks of overfitting associated with traditional interpolation-based techniques. Four classifiers, XGBoost, RandomForest, AdaBoost and VotingClassifier, were evaluated using the original data set and variants with data augmentation. The RandomForest classifier achieved the best performance when using data generated with SynKGen (accuracy: 0.9949, precision:0.9899) outperforming the results obtained with ADASYN and SMOTE. Experimental results demonstrate that SynKGen improves the effectiveness of credit card bank fraud detection. These findings highlight the importance of data augmentation strategies to optimize classifier performance in financial contexts with unbalanced data.

The measurement of urban development in Latin American cities has historically relied on one-dimensional models focused on GDP or density, which are insufficient to guide policies in heterogeneous and unequal contexts. This study designs and applies an empirical and comparative model with a socioeconomic approach to evaluate urban development in Lima, Medellín, and San Salvador in 2020–2024. Seventeen indicators that are comparable, objective, and publicly available in the three cities are integrated and normalized using min–max, and composite indices are estimated using three methods: equal weighting, CRITIC, and Entropy. It was found that Lima consistently shows the greatest relative progress (≈6–7%); Medellín consolidates high levels with moderate increases (≈2–4%); and San Salvador records the highest proportional growth under the Entropy method (≈11%). These findings are consistent with citizen perceptions obtained from a survey of 1170 participants (April–August 2025), according to which, on average, 60% recognize the existence of urban growth in the three cities. The proposed model is reproducible and transparent, contributing significantly to year-on-year monitoring and the prioritization of interventions by local and national governments by enabling short- and medium-term readings, in line with the Sustainable Development Goals (SDGs).