37 citations · 224 across the 83 of their papers we have counts for
13 papers · 1 filter
On the Evolution of Boomerang Uniformity in Cryptographic S-boxes
Marko Djurasevic, Domagoj Jakobovic, Luca Mariot +2
S-boxes are an important primitive that help cryptographic algorithms to be resilient against various attacks. The resilience against specific attacks can be connected with a certa…
Evolutionary Strategies for the Design of Binary Linear Codes
Claude Carlet, Luca Mariot, Luca Manzoni +1
The design of binary error-correcting codes is a challenging optimization problem with several applications in telecommunications and storage, which has also been addressed with me…
Going In Style: Audio Backdoors Through Stylistic Transformations
Stefanos Koffas, Luca Pajola, Stjepan Picek +1
This work explores stylistic triggers for backdoor attacks in the audio domain: dynamic transformations of malicious samples through guitar effects. We first formalize stylistic tr…
On the Vulnerability of Data Points under Multiple Membership Inference Attacks and Target Models
Mauro Conti, Jiaxin Li, Stjepan Picek
Membership Inference Attacks (MIAs) infer whether a data point is in the training data of a machine learning model. It is a threat while being in the training data is private infor…
DARWIN: Survival of the Fittest Fuzzing Mutators
Patrick Jauernig, Domagoj Jakobovic, Stjepan Picek +2
Fuzzing is an automated software testing technique broadly adopted by the industry. A popular variant is mutation-based fuzzing, which discovers a large number of bugs in practice.…
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering
Rui Wang, Xingkai Wang, Huanhuan Chen +4
Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing…
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks
Mauro Conti, Jiaxin Li, Stjepan Picek +1
Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs), aggregate the message of nodes' neighbors and structure information to acquire expressive representa…
Dynamic Backdoors with Global Average Pooling
Stefanos Koffas, Stjepan Picek, Mauro Conti
Outsourced training and machine learning as a service have resulted in novel attack vectors like backdoor attacks. Such attacks embed a secret functionality in a neural network act…
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…
Evolving Constructions for Balanced, Highly Nonlinear Boolean Functions
Claude Carlet, Marko Djurasevic, Domagoj Jakobovic +2
Finding balanced, highly nonlinear Boolean functions is a difficult problem where it is not known what nonlinearity values are possible to be reached in general. At the same time,…
Evolutionary Construction of Perfectly Balanced Boolean Functions
Luca Mariot, Stjepan Picek, Domagoj Jakobovic +2
Finding Boolean functions suitable for cryptographic primitives is a complex combinatorial optimization problem, since they must satisfy several properties to resist cryptanalytic…
Modeling Strong Physically Unclonable Functions with Metaheuristics
Carlos Coello Coello, Marko Djurasevic, Domagoj Jakobovic +2
Evolutionary algorithms have been successfully applied to attacking Physically Unclonable Functions (PUFs). CMA-ES is recognized as the most powerful option for a type of attack ca…
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
Jing Xu, Rui Wang, Stefanos Koffas +2
Graph Neural Networks (GNNs) are a class of deep learning-based methods for processing graph domain information. GNNs have recently become a widely used graph analysis method due t…