https://hdl.handle.net/10259/3847 Tue, 18 Aug 2026 13:04:58 GMT 2026-08-18T13:04:58Z https://hdl.handle.net/10259/11926 Toward AI-driven IoT cybersecurity: A preprocessing framework for benchmark datasets Martínez Fuentes, Virginia; Arroyo Puente, Ángel; Granados López, Diego; Herrero Cosío, Álvaro The rapid expansion of Internet of Things (IoT) systems, found in environments such as smart homes, poses growing cybersecurity challenges. In response, research has examined the role of artificial intelligence, particularly machine learning, in enhancing IoT security. To support this effort, machine learning models have been developed and evaluated on benchmark datasets. However, preparing datasets for machine learning requires preprocessing techniques that are tailored to the specific characteristics of the data. In this context, exploratory data analysis provides insights into dataset structure and distribution, thereby supporting informed preprocessing decisions prior to modeling. Accordingly, this study introduces a reproducible five-step preprocessing framework for IoT cybersecurity datasets and demonstrates its application to the NF-ToN-IoT V1 dataset. The proposed framework is organized into two phases: an exploratory data analysis phase consisting of (1) dataset overview and identification of categorical and numerical features, (2) analysis of missing and zero values, (3) assessment of categorical feature distributions, and (4) assessment of numerical feature distributions; and a preprocessing phase consisting of (5) proportional stratified random downsampling to produce a reduced dataset that preserves the original class distribution. By establishing a systematic, data-driven framework, this study contributes to the preparation of structured datasets for attack detection in IoT environments, with potential applications in smart homes. Wed, 01 Apr 2026 00:00:00 GMT https://hdl.handle.net/10259/11926 2026-04-01T00:00:00Z https://hdl.handle.net/10259/11860 Transformer-based classification of IoT network traffic with flow-to-window aggregation Martin Reizabal, Sergio; Caballero Quiroga, Adrian; Gil Arroyo, Beatriz; Basurto Hornillos, Nuño; Ruiz González, Rubén The explosive growth of the IoT has led to an increasingly complex and heterogeneous network traffic, posing major challenges for intrusion detection. Most existing machine learning and deep learning approaches model network traffic at the level of individual flows, which limits their ability to capture contextual relationships among concurrent communications. This paper introduces a Transformer-based framework for IoT intrusion detection that aggregates network flows into fixed-duration windows and treats each flow as a token within the input sequence. The self-attention mechanism captures contextual relationships among concurrent flows, enabling effective modeling of temporal dependencies without recurrence. Experiments conducted on the CICIoT2023 dataset show that the proposed model achieves a weighted F1-score of 97.9% and a macro ROC–AUC of 99.6% under temporally blocked cross-validation, while maintaining high computational efficiency. These results demonstrate that flow-to-window aggregation combined with self-attention provides a robust and scalable foundation for IoT network security, suitable for deployment in edge and smart-home environments. Sun, 01 Mar 2026 00:00:00 GMT https://hdl.handle.net/10259/11860 2026-03-01T00:00:00Z https://hdl.handle.net/10259/11732 Detecting copper-based fungicides in vineyards by means of hyperspectral imagery Sánchez Alonso, Ramón; Rad Moradillo, Juan Carlos; Cambra Baseca, Carlos; Barros García, Rocío; Herrero Cosío, Álvaro Fungal diseases affecting vineyards are commonly controlled using copper-based fungicides. Inaccurate application of these products usually leads to accumulations of copper in the soil. The use of spectral images in vineyards is a tool that can help in the correct application of fungicides to improve their efficiency and effectiveness. To do that, a solution is required to identify the copper deposited on the vine leaf. To bridge this gap, the present work compares images obtained with a hyperspectral camera (Pika L, Resonon) of vineyard leaves (Vitis vinifera L.) cv. Tempranillo treated with two copper-based products, Cuprantol duo (Syngenta, CH) and Cuprocol (Syngenta, CH). Treated leaves with both products and the corresponding blanks made with distilled water were compared. Most of the differences between treatments and products are found in the near-infrared region (700–740 nm), the green region (550 nm) and the region of (620–640 nm). Maximal spectral variations appeared in the range of 711.16–758.27 nm for wet status products, which allowed to differentiate between the areas treated with copper-based products from the blanks without product. We can conclude that using hyperspectral imagery is possible to detect leave areas treated with copper-based fungicides immediately (wet treatment) after application Mon, 01 Dec 2025 00:00:00 GMT https://hdl.handle.net/10259/11732 2025-12-01T00:00:00Z https://hdl.handle.net/10259/11727 SEM-EDS and hyperspectral images of vine leaves treated with antifungal products Sánchez Alonso, Ramón; Rad Moradillo, Juan Carlos; Cambra Baseca, Carlos; Castroviejo Fernández, Mª Pilar; Barros García, Rocío; Herrero Cosío, Álvaro Scanning electron microscope, better known by its acronym as SEM, is a very useful technique for obtaining highresolution images of the surface of a sample. Hyperspectral imaging provides precise information for analysing vineyard vegetation that could help in improving pesticide application in precision viticulture technics. The present dataset is based on images of vineyard leaves, taken with both technics. The leaves of the cv. Tempranillo, proceeding from a vineyard located inside of the Cigales Denomination of Origin, in north-central Spain, were treated with two Cu-containing products: ZZ Cuprocol (70 % w/v copper oxychloride) and Cuprantol Duo (14 % w/w copper oxychloride, 14 % w/w copper hydroxide). In addition, a contact pesticide widely used in intensive and traditional viticulture based on Folpet, copper-free but containing sulphur and chlorine, has been tested in its commercial form, Vitipec Blue (Cymoxanil 6 % w/w, Folpet 37.5 % w/w, Ascenza, PT). Three dilutions were prepared, one of each compound, at the actual field application concentration of 1.33 g/L. The leaves were sampled and processed during the 2023 season. These leaves were taken from the central part of representative shoots of the vine canopy, with east and west exposures. After the application of the pesticide dilutions, images of the leaves were taken with a 300-channel hyperspectral camera (Pika L, Resonon) using a mechanical bench synchronized with the camera. Then the SEM analysis was carried after prepare the samples. Hence, such imagery is provided in the present dataset, based on the images taken from the leaves with both technics Wed, 01 Oct 2025 00:00:00 GMT https://hdl.handle.net/10259/11727 2025-10-01T00:00:00Z