50 citations · 79 across the 14 of their papers we have counts for
16 papers
Automotive Hardware Attacks: An Architect's Guide to TARA
Jakub Breier, Xiaolu Hou
Threat analysis and risk assessment (TARA) according to ISO/SAE 21434 treats implementation-level hardware attacks inconsistently. We show that two of the three attack-feasibility…
Guess My Weight: Profiled Side-Channel Recovery of Floating-Point Neural-Network Weights
Timon Lumír Fillo, Ján Mikulec, Anubhab Baksi +2
Neural-network parameters deployed on embedded devices may be exposed through physical side-channel leakage during inference. Existing side-channel attacks on floating-point neural…
A Constant-Time Implementation Methodology for Activation Functions on Microcontrollers
Andrii Tyvodar, Andreas Rechberger, Dirmanto Jap +4
Embedded neural-network inference can leak information through timing side channels, including leakage caused by the evaluation of activation functions. This work proposes a consta…
Characterizing the Fault Response of the Intel Neural Compute Stick 2 Under Single-Pulse Electromagnetic Fault Injection
Štefan Kučerák, Jakub Breier, Xiaolu Hou
Vision processing units and other commercial neural-network inference accelerators are increasingly deployed in safety-relevant edge applications, but their fault response under tr…
Beyond TVLA: Anderson-Darling Leakage Assessment for Neural Network Side-Channel Leakage Detection
Ján Mikulec, Jakub Breier, Xiaolu Hou
Test Vector Leakage Assessment (TVLA) is widely used for side-channel leakage detection, but its reliance on Welch's t-test makes it primarily sensitive to differences in the means…
Make Shuffling Great Again: A Side-Channel Resistant Fisher-Yates Algorithm for Protecting Neural Networks
Leonard Puškáč, Marek Benovič, Jakub Breier +1
Neural network models implemented in embedded devices have been shown to be susceptible to side-channel attacks (SCAs), allowing recovery of proprietary model parameters, such as w…
Side-Channel Analysis of OpenVINO-based Neural Network Models
Dirmanto Jap, Jakub Breier, Zdenko Lehocký +2
Embedded devices with neural network accelerators offer great versatility for their users, reducing the need to use cloud-based services. At the same time, they introduce new secur…
DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers
C. A. Martínez-Mejía, J. Solano, J. Breier +2
Machine Learning using neural networks has received prominent attention recently because of its success in solving a wide variety of computational tasks, in particular in the field…
DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks
Patrik Velčický, Jakub Breier, Mladen Kovačević +1
Fault injection attacks are a potent threat against embedded implementations of neural network models. Several attack vectors have been proposed, such as misclassification, model e…
A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks
Jakub Breier, Dirmanto Jap, Xiaolu Hou +1
Model extraction attacks have been widely applied, which can normally be used to recover confidential parameters of neural networks for multiple layers. Recently, side-channel anal…
FooBaR: Fault Fooling Backdoor Attack on Neural Network Training
Jakub Breier, Xiaolu Hou, Martín Ochoa +1
Neural network implementations are known to be vulnerable to physical attack vectors such as fault injection attacks. As of now, these attacks were only utilized during the inferen…
SNIFF: Reverse Engineering of Neural Networks with Fault Attacks
Jakub Breier, Dirmanto Jap, Xiaolu Hou +2
Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of the device during the computation, resulting in a ch…
DeepLaser: Practical Fault Attack on Deep Neural Networks
Jakub Breier, Xiaolu Hou, Dirmanto Jap +3
As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults and attacks become a…
Construction of Arakelov-modular Lattices from Number Fields
Xiaolu Hou
An Arakelov-modular lattice of level , where is a positive integer, is an modular lattice constructed from a fractional ideal of a CM field such that the lattic…
Modular Lattices from a Variation of Construction A over Number Fields
Xiaolu Hou, Frédérique Oggier
We consider a variation of Construction A of lattices from linear codes based on two classes of number fields, totally real and CM Galois number fields. We propose a generic constr…
Construction of Arakelov-modular Lattices over Totally Definite Quaternion Algebras
Xiaolu Hou
We study ideal lattices constructed from totally definite quaternion algebras over totally real number fields, and generalize the definition of Arakelov-modular lattices over numbe…