In recent years, smartphones have become the main computing tool for most of the population, making them an ideal tool in many areas. Most of these smartphones are equipped with cutting-edge hardware on their digital cameras, sensors and processors. For this reason, this paper discusses the possibility of using smartphones as an accessible and accurate tool, focusing on the measurement of light, which is an element that has a high impact on human behavior, which promotes conformance and safety, or alters human physiology when it is inappropriate. To carry out this study, three different ways to measure light through smartphones have been checked: the ambient light sensor, the digital camera and an external Bluetooth luxmeter connected with the smartphone. As a result, the accuracy of these methods has been compared to check if they can be used as accurate measurement tools.

Continuous authentication (CA) is the process to verify the user’s identity regularly without their active participation. CA is becoming increasingly important in the mobile environment in which traditional one-time authentication methods are susceptible to attacks, and devices can be subject to loss or theft. The existing literature reports CA approaches using various input data from typing events, sensors, gestures, or other user interactions. However, there is significant diversity in the methodology and systems used, to the point that studies differ significantly in the features used, data acquisition, extraction, training, and evaluation. It is, therefore, difficult to establish a reliable basis to compare CA methods. In this study, keystroke mechanics of the public HMOG dataset were used to train seven different machine learning classifiers, including ensemble methods (RFC, ETC, and GBC), instance-based (k-NN), hyperplane optimization (SVM), decision trees (CART), and probabilistic methods (naïve Bayes). The results show that a small number of key events and measurements can be used to return predictions of user identity. Ensemble algorithms outperform others regarding the CA mobile keystroke classification problem, with GBC returning the best statistical results.

In the era of increasing digitalisation, organisations face the critical challenge of detecting anomalies in large volumes of data, which may indicate suspicious activities. To address this challenge, audit engagements are conducted regularly, and internal auditors and purchasing specialists seek innovative methods to streamline these processes. This study introduces a methodology to prioritise the investigation of anomalies identified in two large real-world purchase datasets. The primary objective is to enhance the effectiveness of companies’ control efforts and improve the efficiency of anomaly detection tasks. The approach begins with a comprehensive exploratory data analysis, followed by the application of unsupervised machine learning techniques to identify anomalies. A univariate analysis is performed using the z-Score index and the DBSCAN algorithm, while multivariate analysis employs k-Means clustering and Isolation Forest algorithms. Additionally, the Silhouette index is used to evaluate the quality of the clustering, ensuring each method produces a prioritised list of candidate transactions for further review. To refine this process, an ensemble prioritisation framework is developed, integrating multiple methods. Furthermore, explainability tools such as SHAP are utilised to provide actionable insights and support specialists in interpreting the results. This methodology aims to empower organisations to detect anomalies more effectively and streamline the audit process.

Numerous techniques have been developed in order to prevent attacks on web servers. Anomaly detection techniques are based on models of normal user and application behavior, interpreting deviations from the established pattern as indications of malicious activity. In this work, a systematic review of the use of anomaly detection techniques in the prevention and detection of web attacks is undertaken; in particular, we used the standardized method of a systematic review of literature in the field of computer science, proposed by Kitchenham. This method is applied to a set of 88 papers extracted from a total of 8041 reviewed papers, which have been published in notable journals. This paper discusses the process carried out in this systematic review, as well as the results and findings obtained to identify the current state of the art of web anomaly detection.

A quality management system (QMS) allows an organization to analyze the needs of its customers and to define the appropriate processes for efficient production and service delivery. Customer satisfaction is an important part of this system, with a significant impact on its corporate sustainability. The purpose of this research is a proposal for assessing whether the QMS of an organization meets the requirements defined in the standard ISO 9001:2015. The development of the proposal follows a methodology based on the paradigm of design science research where we identify the problem and the motivation and the objectives are defined as a solution to the problem, and as a consequence, we developed an expert system based on a previously identified ISO 9001:2015 process map and transferred it to a knowledge base. The system allows the determination of the degree of compliance with the requirements of the standard: this provides valuable information to organizations, allowing them to implement improvement measures. The expert system is limited in terms of the set of identified inputs, outputs, and acceptance criteria, but its effectiveness was demonstrated through the evaluation of the system.