Zerrendatu Artikuluak-Ingeniaritza honen arabera: ikerketa taldea "Análisis de datos y ciberseguridad"
55-tik 1-20 emaitza erakusten
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Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data
(MDPI, 2021)Predictive maintenance (PdM) has the potential to reduce industrial costs by anticipating failures and extending the work life of components. Nowadays, factories are monitoring their assets and most collected data belong ... -
Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control
(MDPI AG, 2019)This paper introduces a novel data driven yaw control algorithm synthesis method based on Reinforcement Learning (RL) for a variable pitch variable speed wind turbine. Yaw control has not been extendedly studied in the ... -
Bending rigidity, sound propagation and ripples in flat graphene
(Springer Nature, 2024)Many of the applications of graphene rely on its uneven stiffness and high thermal conductivity, but the mechanical properties of graphene—and, in general, of all two-dimensional materials—are still not fully understood. ... -
Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation
(MDPI, 2023)In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is ... -
A Big Data implementation of the MANTIS Reference Architecture for Predictive Maintenance
(Sage Journals, 2019) -
The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process
(Springer Verlag, 2019)Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes.Through this, it is intended to improve the management of resources, decision-making, ... -
Clustered federated learning architecture for network anomaly detection in large scale heterogeneous IoT networks
(Elsevier, 2023)There is a growing trend of cyberattacks against Internet of Things (IoT) devices; moreover, the sophistication and motivation of those attacks is increasing. The vast scale of IoT, diverse hardware and software, and being ... -
Comparative assessment of synthetic time series generation approaches in healthcare: leveraging patient metadata for accurate data synthesis
(Springer Nature, 2024)Background Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with ... -
Cookies from the Past: Timing Server-Side Request Processing Code for History Sniffing
(ACM, 2020)Cookies were originally introduced as a way to provide state awareness to websites, and are now one of the backbones of the current web. However, their use is not limited to store the login information or to save the current ... -
Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights
(MDPI, 2024)PEO constitutes a promising surface technology for the development of protective and functional ceramic coatings on lightweight alloys. Despite its interesting advantages, including enhanced wear and corrosion resistances ... -
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
(Springer Science+Business Media, LLC, 2022)Given the growing amount of industrial data in the 4th industrial revolution, deep learning solutions have become popular for predictive maintenance (PdM) tasks, which involve monitoring assets to anticipate their requirements ... -
Deep packet inspection for intelligent intrusion detection in software-defined industrial networks: A proof of concept
(Oxford Academic, 2020)Specifically tailored industrial control systems (ICSs) attacks are becoming increasingly sophisticated, accentuating the need of ICS cyber security. The nature of these systems makes traditional IT security measures not ... -
Deobfuscating leetspeak with deep learning to improve spam filtering
(UNIR - Universidad Internacional de La Rioja, 2023)The evolution of anti-spam filters has forced spammers to make greater efforts to bypass filters in order to distribute content over networks. The distribution of content encoded in images or the use of Leetspeak are ... -
Detection and Visualization of Android Malware Behavior
(Hindawi Publishing Corporation, 2016)Malware analysts still need to manually inspect malware samples that are considered suspicious by heuristic rules. They dissect software pieces and look for malware evidence in the code. The increasing number of malicious ... -
Different approaches for the detection of SSH anomalous connections
(Oxford Academic, 2016)The Secure Shell Protocol (SSH) is a well-known standard protocol, mainly used for remotely accessing shell accounts on Unix-like operating systems to perform administrative tasks. As a result, the SSH service has been an ... -
Error estimation in current noisy quantum computers
(Springer Nature, 2024)One of the main important features of the noisy intermediate-scale quantum (NISQ) era is the correct evaluation and consideration of errors. In this paper, we analyse the main sources of errors in current (IBM) quantum ... -
Estimación cualitativa de la rugosidad mediante algoritmos de aprendizaje automático en una operación de taladrado
(Federación de Asociaciones de Ingenieros Industriales de España, 2020) -
Fuzzing the Internet of Things: A Review on the Techniques and Challenges for Efficient Vulnerability Discovery in Embedded Systems
(IEEE, 2021)With a growing number of embedded devices that create, transform and send data autonomously at its core, the Internet-of-Things (IoT) is a reality in different sectors such as manufacturing, healthcare or transportation. ... -
Gotham Testbed: A Reproducible IoT Testbed for Security Experiments and Dataset Generation
(IEEE, 2023)The growing adoption of the Internet of Things (IoT) has brought a significant increase in attacks targeting those devices. Machine learning (ML) methods have shown promising results for intrusion detection; however, the ... -
Impregnation quality diagnosis in Resin Transfer Moulding by machine learning
(Elsevier Ltd., 2021)In recent years, several optimization strategies have been developed which reduce the overall defectiveness of the RTM manufactured part. RTM filling simulations showed that, even using optimized injection strategies, local ...
