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Article: Rapid Growth of Metal-Metal Oxide Core-Shell Structures through Joule Resistive Heating: Morphological, Structural, and Luminescence Characterization.

Ramos-Justicia, Juan Francisco / Urbieta, Ana / Fernández, Paloma

2023 Volume 17, Issue 1

Abstract: The aim of this study is to prove that resistive heating enables the synthesis of metal/metal oxide composites in the form of core-shell structures. The thickness and morphology of the oxide layer depends strongly on the nature of the metal, but the ... ...

Abstract	The aim of this study is to prove that resistive heating enables the synthesis of metal/metal oxide composites in the form of core-shell structures. The thickness and morphology of the oxide layer depends strongly on the nature of the metal, but the influences of parameters such as the time and current profiles and the presence of an external field have also been investigated. The systems chosen for the present study are Zn/ZnO, Ti/TiO
Language	English
Publishing date	2023-12-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma17010208
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Optical Properties of 2D Micro- and Nanostructures of ZnO:K.

Ariza, Rocío / Urbieta, Ana / Solis, Javier / Fernández, Paloma

Materials (Basel, Switzerland)

2022 Volume 15, Issue 21

Abstract: ZnO nano- and microstructures doped with K were grown by the Vapor-Solid method. Wires and needles are the main morphology observed, although some structures in the form of ribbons and triangular plates were also obtained. Besides these, ball-shaped ... ...

Abstract	ZnO nano- and microstructures doped with K were grown by the Vapor-Solid method. Wires and needles are the main morphology observed, although some structures in the form of ribbons and triangular plates were also obtained. Besides these, ball-shaped structures which grow around a central wire were also detected. Raman and cathodoluminescence investigations suggest that variations in morphology, crystalline quality and luminescence emissions are related to the different lattice positions that K occupies depending on its concentration in the structures. When the amount is low, K ions mainly incorporate as interstitials (K
Language	English
Publishing date	2022-11-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma15217733
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Article ; Online: Revisiting the Feasibility of Public Key Cryptography in Light of IIoT Communications.

Astorga, Jasone / Barcelo, Marc / Urbieta, Aitor / Jacob, Eduardo

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 7

Abstract: Digital certificates are regarded as the most secure and scalable way of implementing authentication services in the Internet today. They are used by most popular security protocols, including Transport Layer Security (TLS) and Datagram Transport Layer ... ...

Abstract	Digital certificates are regarded as the most secure and scalable way of implementing authentication services in the Internet today. They are used by most popular security protocols, including Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). The lifecycle management of digital certificates relies on centralized Certification Authority (CA)-based Public Key Infrastructures (PKIs). However, the implementation of PKIs and certificate lifecycle management procedures in Industrial Internet of Things (IIoT) environments presents some challenges, mainly due to the high resource consumption that they imply and the lack of trust in the centralized CAs. This paper identifies and describes the main challenges to implement certificate-based public key cryptography in IIoT environments and it surveys the alternative approaches proposed so far in the literature to address these challenges. Most proposals rely on the introduction of a Trusted Third Party to aid the IIoT devices in tasks that exceed their capacity. The proposed alternatives are complementary and their application depends on the specific challenge to solve, the application scenario, and the capacities of the involved IIoT devices. This paper revisits all these alternatives in light of industrial communication models, identifying their strengths and weaknesses, and providing an in-depth comparative analysis.
MeSH term(s)	Communication ; Feasibility Studies ; Industry ; Internet of Things ; Trust
Language	English
Publishing date	2022-03-27
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22072561
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Book ; Online: Growth of Zr/ZrO2 core-shell structures by Fast Thermal Oxidation

Ramos-Justicia, J. F. / Ballester-andújar, J. L. / Urbieta, A. / Fernández, P.

2023

Abstract: This research has been conducted to characterize and validate the resistive heating as a synthesis method for zirconium oxides (ZrO$_2$). A wire of Zr has been oxidized to form a core shell structure, in which the core is the metal wire, and the shell is ...

Abstract	This research has been conducted to characterize and validate the resistive heating as a synthesis method for zirconium oxides (ZrO$_2$). A wire of Zr has been oxidized to form a core shell structure, in which the core is the metal wire, and the shell is an oxide layer around 10${\mu}$m thick. The characterization This research has been conducted to characterize and validate the resistive heating as a synthesis method for zirconium oxides (ZrO$_2$). A wire of Zr has been oxidized to form a core shell structure, in which the core is the metal wire, and the shell is an oxide layer around 10${\mu}$m thick. The characterization of the samples has been performed by means of Scanning Electron Microscopy (SEM). The chemical composition was analysed by X-ray spectroscopy (EDX). X-ray diffraction (XRD) and Raman spectroscopy have been used to assess crystallinity and crystal structure. Photoluminescence (PL) and cathodoluminescence (CL) measurements have allowed us to study the distribution of defects along the shell, and to confirm the degree of uniformity. The oxygen vacancies, either as isolated defects or forming complexes with impurities, play a determinant role in the luminescent processes. Colour centres, mainly electron centres as F, F$_A$ and F$_{AA}$, give rise to several visible emissions extending from blue to green, with main components around 2eV, 2.4-2.5eV and 2.7eV. The differences between PL and CL are also discussed. Comment: 15 pages, 12 figures
Keywords	Condensed Matter - Materials Science
Subject code	620
Publishing date	2023-02-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Sneaky Spikes

Abad, Gorka / Ersoy, Oguzhan / Picek, Stjepan / Urbieta, Aitor

Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data

2023

Abstract: Deep neural networks (DNNs) have demonstrated remarkable performance across various tasks, including image and speech recognition. However, maximizing the effectiveness of DNNs requires meticulous optimization of numerous hyperparameters and network ... ...

Abstract	Deep neural networks (DNNs) have demonstrated remarkable performance across various tasks, including image and speech recognition. However, maximizing the effectiveness of DNNs requires meticulous optimization of numerous hyperparameters and network parameters through training. Moreover, high-performance DNNs entail many parameters, which consume significant energy during training. In order to overcome these challenges, researchers have turned to spiking neural networks (SNNs), which offer enhanced energy efficiency and biologically plausible data processing capabilities, rendering them highly suitable for sensory data tasks, particularly in neuromorphic data. Despite their advantages, SNNs, like DNNs, are susceptible to various threats, including adversarial examples and backdoor attacks. Yet, the field of SNNs still needs to be explored in terms of understanding and countering these attacks. This paper delves into backdoor attacks in SNNs using neuromorphic datasets and diverse triggers. Specifically, we explore backdoor triggers within neuromorphic data that can manipulate their position and color, providing a broader scope of possibilities than conventional triggers in domains like images. We present various attack strategies, achieving an attack success rate of up to 100\% while maintaining a negligible impact on clean accuracy. Furthermore, we assess these attacks' stealthiness, revealing that our most potent attacks possess significant stealth capabilities. Lastly, we adapt several state-of-the-art defenses from the image domain, evaluating their efficacy on neuromorphic data and uncovering instances where they fall short, leading to compromised performance.
Keywords	Computer Science - Cryptography and Security ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-02-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Substantial Burden of Nonmedically Attended RSV Infection in Healthy-Term Infants: An International Prospective Birth Cohort Study.

Hak, Sarah F / Venekamp, Roderick P / Billard, Marie-Noëlle / van Houten, Marlies A / Pollard, Andrew J / Heikkinen, Terho / Cunningham, Steve / Millar, Margaret / Martinón-Torres, Federico / Dacosta-Urbieta, Ana / Bont, Louis J / Wildenbeest, Joanne G

The Journal of infectious diseases

2024 Volume 229, Issue Supplement_1, Page(s) S40–S50

Abstract: Background: During the first year of life, 1 in 4 infants develops a symptomatic respiratory syncytial virus (RSV) infection, yet only half seek medical attention. The current focus on medically attended RSV therefore underrepresents the true societal ... ...

Abstract	Background: During the first year of life, 1 in 4 infants develops a symptomatic respiratory syncytial virus (RSV) infection, yet only half seek medical attention. The current focus on medically attended RSV therefore underrepresents the true societal burden of RSV. We assessed the burden of nonmedically attended RSV infections and compared with medically attended RSV. Methods: We performed active RSV surveillance until the age of 1 year in a cohort (n = 993) nested within the Respiratory Syncytial Virus Consortium in EUrope (RESCEU) prospective birth cohort study enrolling healthy term-born infants in 5 European countries. Symptoms, medication use, wheezing, and impact on family life were analyzed. Results: For 97 of 120 (80.1%) nonmedically attended RSV episodes, sufficient data were available for analysis. In 50.5% (49/97), symptoms lasted ≥15 days. Parents reported impairment in usual daily activities in 59.8% (58/97) of episodes; worries, 75.3% (73/97); anxiety, 34.0% (33/97); and work absenteeism, 10.8% (10/93). Compared with medically attended RSV (n = 102, 9 hospital admissions), Respiratory Syncytial Virus NETwork (ReSViNET) severity scores were lower (3.5 vs 4.6, P < .001), whereas duration of respiratory symptoms and was comparable. Conclusions: Even when medical attendance is not required, RSV infection poses a substantial burden to infants, families, and society. These findings are important for policy makers when considering the implementation of RSV immunization. Clinical Trials Registration. ClinicalTrials.gov (NCT03627572).
MeSH term(s)	Infant ; Humans ; Respiratory Syncytial Virus Infections ; Cohort Studies ; Prospective Studies ; Respiratory Syncytial Virus, Human ; Europe/epidemiology ; Hospitalization
Language	English
Publishing date	2024-03-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	3019-3
ISSN	1537-6613 ; 0022-1899
ISSN (online)	1537-6613
ISSN	0022-1899
DOI	10.1093/infdis/jiad477
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Book ; Online: Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networks

Sáez-de-Cámara, Xabier / Flores, Jose Luis / Arellano, Cristóbal / Urbieta, Aitor / Zurutuza, Urko

2023

Abstract: 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 typically placed in ... ...

Abstract	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 typically placed in uncontrolled environments make traditional IT security mechanisms such as signature-based intrusion detection and prevention systems challenging to integrate. They also struggle to cope with the rapidly evolving IoT threat landscape due to long delays between the analysis and publication of the detection rules. Machine learning methods have shown faster response to emerging threats; however, model training architectures like cloud or edge computing face multiple drawbacks in IoT settings, including network overhead and data isolation arising from the large scale and heterogeneity that characterizes these networks. This work presents an architecture for training unsupervised models for network intrusion detection in large, distributed IoT and Industrial IoT (IIoT) deployments. We leverage Federated Learning (FL) to collaboratively train between peers and reduce isolation and network overhead problems. We build upon it to include an unsupervised device clustering algorithm fully integrated into the FL pipeline to address the heterogeneity issues that arise in FL settings. The architecture is implemented and evaluated using a testbed that includes various emulated IoT/IIoT devices and attackers interacting in a complex network topology comprising 100 emulated devices, 30 switches and 10 routers. The anomaly detection models are evaluated on real attacks performed by the testbed's threat actors, including the entire Mirai malware lifecycle, an additional botnet based on the Merlin command and control server and other red-teaming tools performing scanning activities and multiple attacks targeting the emulated devices.
Keywords	Computer Science - Cryptography and Security
Subject code	006
Publishing date	2023-03-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: On the Security & Privacy in Federated Learning

Abad, Gorka / Picek, Stjepan / Ramírez-Durán, Víctor Julio / Urbieta, Aitor

2021

Abstract: Recent privacy awareness initiatives such as the EU General Data Protection Regulation subdued Machine Learning (ML) to privacy and security assessments. Federated Learning (FL) grants a privacy-driven, decentralized training scheme that improves ML ... ...

Abstract	Recent privacy awareness initiatives such as the EU General Data Protection Regulation subdued Machine Learning (ML) to privacy and security assessments. Federated Learning (FL) grants a privacy-driven, decentralized training scheme that improves ML models' security. The industry's fast-growing adaptation and security evaluations of FL technology exposed various vulnerabilities that threaten FL's confidentiality, integrity, or availability (CIA). This work assesses the CIA of FL by reviewing the state-of-the-art (SoTA) and creating a threat model that embraces the attack's surface, adversarial actors, capabilities, and goals. We propose the first unifying taxonomy for attacks and defenses and provide promising future research directions.
Keywords	Computer Science - Cryptography and Security
Publishing date	2021-12-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Sniper Backdoor

Abad, Gorka / Paguada, Servio / Ersoy, Oguzhan / Picek, Stjepan / Ramírez-Durán, Víctor Julio / Urbieta, Aitor

Single Client Targeted Backdoor Attack in Federated Learning

2022

Abstract: Federated Learning (FL) enables collaborative training of Deep Learning (DL) models where the data is retained locally. Like DL, FL has severe security weaknesses that the attackers can exploit, e.g., model inversion and backdoor attacks. Model inversion ...

Abstract	Federated Learning (FL) enables collaborative training of Deep Learning (DL) models where the data is retained locally. Like DL, FL has severe security weaknesses that the attackers can exploit, e.g., model inversion and backdoor attacks. Model inversion attacks reconstruct the data from the training datasets, whereas backdoors misclassify only classes containing specific properties, e.g., a pixel pattern. Backdoors are prominent in FL and aim to poison every client model, while model inversion attacks can target even a single client. This paper introduces a novel technique to allow backdoor attacks to be client-targeted, compromising a single client while the rest remain unchanged. The attack takes advantage of state-of-the-art model inversion and backdoor attacks. Precisely, we leverage a Generative Adversarial Network to perform the model inversion. Afterward, we shadow-train the FL network, in which, using a Siamese Neural Network, we can identify, target, and backdoor the victim's model. Our attack has been validated using the MNIST, F-MNIST, EMNIST, and CIFAR-100 datasets under different settings -- achieving up to 99\% accuracy on both source (clean) and target (backdoor) classes and against state-of-the-art defenses, e.g., Neural Cleanse, opening a novel threat model to be considered in the future.
Keywords	Computer Science - Cryptography and Security
Subject code	006
Publishing date	2022-03-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Gotham Testbed

Sáez-de-Cámara, Xabier / Flores, Jose Luis / Arellano, Cristóbal / Urbieta, Aitor / Zurutuza, Urko

a Reproducible IoT Testbed for Security Experiments and Dataset Generation

2022

Abstract: The scarcity of available Internet of Things (IoT) datasets remains a limiting factor in developing machine learning based security systems. Static datasets get outdated due to evolving IoT threat landscape. Meanwhile, the testbeds used to generate them ... ...

Abstract	The scarcity of available Internet of Things (IoT) datasets remains a limiting factor in developing machine learning based security systems. Static datasets get outdated due to evolving IoT threat landscape. Meanwhile, the testbeds used to generate them are rarely published. This paper presents the Gotham testbed, a reproducible and flexible network security testbed, implemented as a middleware over the GNS3 emulator, that is extendable to accommodate new emulated devices, services or attackers. The testbed is used to build an IoT scenario composed of 100 emulated devices communicating via MQTT, CoAP and RTSP protocols in a topology composed of 30 switches and 10 routers. The scenario presents three threat actors, including the entire Mirai botnet lifecycle and additional red-teaming tools performing DoS, scanning and various attacks targeting the MQTT and CoAP protocols. The generated network traffic and application logs can be used to capture datasets containing legitimate and attacking traces. We hope that researchers can leverage the testbed and adapt it to include other types of devices and state-of-the-art attacks to generate new datasets that reflect the current threat landscape and IoT protocols. The source code to reproduce the scenario is publicly accessible.
Keywords	Computer Science - Cryptography and Security
Subject code	303
Publishing date	2022-07-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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