Description
The success of horizontal side-channel attacks heavily depends on the quality of the traces as well as the correct extraction of interest areas, which are expected to contain relevant leakages. If former is insufficient, this will consequently degrade the identification capability of potential leakage candidates and often render attacks inapplicable. This work assess the relevance of neural networks in the unsupervised context of horizontal attacks to mitigate noise artefacts from the input signal by proposing two methods with alternative training objectives. Their application results in enhanced traces quality and better exploitability using clustering-based horizontal attacks.
Infos pratiques
Prochains exposés
-
Covert Communication Channels Based On Hardware Trojans: Open-Source Dataset and AI-Based Detection
Orateur : Alan Díaz Rizo - Sorbonne Université Lip6
The threat of Hardware Trojan-based Covert Channels (HT-CCs) presents a significant challenge to the security of wireless communications. In this work, we generate in hardware and make open-source a dataset for various HT-CC scenarios. The dataset represents transmissions from a HT-infected RF transceiver hiding a CC that leaks information. It encompasses a wide range of signal impairments, noise[…]-
SemSecuElec
-
Machine learning
-
Hardware trojan
-
-
Measurement the thermal component of clock jitter used as entropy source by TRNGs
Orateur : Arturo GARAY - STMicroelectronics
Introduction Measuring the thermal component of clock jitter as an entropy source for True Random Number Generators (TRNGs) is compulsory for the security and evaluation of clock-jitter based TRNGs. However, identifying and isolating the local thermal noise component from other noise sources, particularly flicker noise, while performing a precise measurement remains a challenge. Current[…]-
SemSecuElec
-
TRNG
-
-
Cryptanalytical extraction of complex Neural Networks in black-box settings
Orateur : Benoit COQUERET - INRIA, Thales CESTI
With the widespread development of artifical intelligence, Deep Neural Networks (DNN) have become valuable intellectual property (IP). In the past few years, software and hardware-based attacks targetting at the weights of the DNN have been introduced allowing potential attacker to gain access to a near-perfect copy of the victim's model. However, these attacks either fail against more complex[…]-
SemSecuElec
-
Side-channel
-
Machine learning
-
-
Advanced techniques for fault injection attacks on integrated circuits
Orateur : Paul Grandamme - Laboratoire Hubert Curien, Université Jean Monnet
The security of integrated circuits is evaluated through the implementation of attacks that exploit their inherent hardware vulnerabilities. Fault injection attacks represent a technique that is commonly employed for this purpose. These techniques permit an attacker to alter the nominal operation of the component in order to obtain confidential information. Firstly, we propose the utilisation of[…]-
SemSecuElec
-
Fault injection
-
-
Side-Channel Based Disassembly on Complex Processors: From Microachitectural Characterization to Probabilistic Models
Orateur : Julien Maillard - CEA
Side-Channel Based Disassembly (SCBD) is a category of Side-Channel Analysis (SCA) that aims at recovering information on the code executed by a processor through the observation of physical side-channels such as power consumption or electromagnetic radiations. While traditional SCA often targets cryptographic keys, SCBD focuses on retrieving assembly code that can hardly be extracted via other[…]-
SemSecuElec
-
Side-channel
-
Hardware reverse
-