139 citations · 265 across the 13 of their papers we have counts for
15 papers
When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control
Roberto Riaño, Gorka Abad, Stjepan Picek +1
Pretrained world models, learned simulators that encode an observation into a latent state and predict how it evolves under actions, are beginning to be reused as off-the-shelf dyn…
MeMark: Membrane-Space Watermarking for Spiking Neural Networks
Roberto Riaño, Gorka Abad, Stjepan Picek +1
Spiking Neural Networks (SNNs) are increasingly distributed as pretrained checkpoints and reused as backbones for new tasks. However, current SNN watermarks are mainly verified aga…
Temporal Poisoning: Clean-Label Backdoors via Event Redistribution in SNNs
Roberto Riaño, Gorka Abad, Stjepan Picek +1
Backdoor attacks on Spiking Neural Networks (SNNs) have primarily assumed dirty-label poisoning, in which triggered training samples are relabeled to an attacker-selected class. We…
Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras
Roberto Riaño, Gorka Abad, Stjepan Picek +1
While security vulnerabilities in traditional Deep Neural Networks (DNNs) have been extensively studied, the susceptibility of Spiking Neural Networks (SNNs) to adversarial attacks…
Context is the Key: Backdoor Attacks for In-Context Learning with Vision Transformers
Gorka Abad, Stjepan Picek, Lorenzo Cavallaro +1
Due to the high cost of training, large model (LM) practitioners commonly use pretrained models downloaded from untrusted sources, which could lead to owning compromised models. In…
Penetration Testing of 5G Core Network Web Technologies
Filippo Giambartolomei, Marc Barceló, Alessandro Brighente +2
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 fash…
Time-Distributed Backdoor Attacks on Federated Spiking Learning
Gorka Abad, Stjepan Picek, Aitor Urbieta
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…
Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networks
Xabier Sáez-de-Cámara, Jose Luis Flores, Cristóbal Arellano +2
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…
Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data
Gorka Abad, Oguzhan Ersoy, Stjepan Picek +1
Deep neural networks (DNNs) have demonstrated remarkable performance across various tasks, including image and speech recognition. However, maximizing the effectiveness of DNNs req…
Too Many Options: A Survey of ABE Libraries for Developers
Aintzane Mosteiro-Sanchez, Marc Barcelo, Jasone Astorga +1
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 attri…
Gotham Testbed: a Reproducible IoT Testbed for Security Experiments and Dataset Generation
Xabier Sáez-de-Cámara, Jose Luis Flores, Cristóbal Arellano +2
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 resul…
Sniper Backdoor: Single Client Targeted Backdoor Attack in Federated Learning
Gorka Abad, Servio Paguada, Oguzhan Ersoy +3
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 attacker…
Securing IIoT using Defence-in-Depth: Towards an End-to-End Secure Industry 4.0
Aintzane Mosteiro-Sanchez, Marc Barcelo, Jasone Astorga +1
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 co…
End to End Secure Data Exchange in Value Chains with Dynamic Policy Updates
Aintzane Mosteiro-Sanchez, Marc Barcelo, Jasone Astorga +1
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 st…
On the Security & Privacy in Federated Learning
Gorka Abad, Stjepan Picek, Víctor Julio Ramírez-Durán +1
Recent privacy awareness initiatives such as the EU General Data Protection Regulation subdued Machine Learning (ML) to privacy and security assessments. Federated Learning (FL) gr…