Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Urbieta, Aitor"'
Thanks to technologies such as virtual network function the Fifth Generation (5G) of mobile networks dynamically allocate resources to different types of users in an on-demand fashion. Virtualization extends up to the 5G core, where software-defined
Externí odkaz:
http://arxiv.org/abs/2403.01871
This paper investigates the vulnerability of spiking neural networks (SNNs) and federated learning (FL) to backdoor attacks using neuromorphic data. Despite the efficiency of SNNs and the privacy advantages of FL, particularly in low-powered devices,
Externí odkaz:
http://arxiv.org/abs/2402.02886
Autor:
Sáez-de-Cámara, Xabier, Flores, Jose Luis, Arellano, Cristóbal, Urbieta, Aitor, Zurutuza, Urko
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 uncontr
Externí odkaz:
http://arxiv.org/abs/2303.15986
Publikováno v:
NDSS Symposium 2024
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
Externí odkaz:
http://arxiv.org/abs/2302.06279
Attribute-based encryption (ABE) comprises a set of one-to-many encryption schemes that allow the encryption and decryption of data by associating it with access policies and attributes. Therefore, it is an asymmetric encryption scheme, and its compu
Externí odkaz:
http://arxiv.org/abs/2209.12742
Autor:
Sáez-de-Cámara, Xabier, Flores, Jose Luis, Arellano, Cristóbal, Urbieta, Aitor, Zurutuza, Urko
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 scarcity of IoT datasets rema
Externí odkaz:
http://arxiv.org/abs/2207.13981
Autor:
Abad, Gorka, Paguada, Servio, Ersoy, Oguzhan, Picek, Stjepan, Ramírez-Durán, Víctor Julio, Urbieta, Aitor
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 inver
Externí odkaz:
http://arxiv.org/abs/2203.08689
Data exchange among value chain partners provides them with a competitive advantage, but the risk of exposing sensitive data is ever-increasing. Information must be protected in storage and transmission to reduce this risk, so only the data producer
Externí odkaz:
http://arxiv.org/abs/2201.06335
Industry 4.0 uses a subset of the IoT, named Industrial IoT (IIoT), to achieve connectivity, interoperability, and decentralization. The deployment of industrial networks rarely considers security by design, but this becomes imperative in smart manuf
Externí odkaz:
http://arxiv.org/abs/2201.05415
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
Externí odkaz:
http://arxiv.org/abs/2112.05423