SearcharxivSearch

arXiv subjects

Ileana Buhan

Publications and source records attributed to Ileana Buhan.

4 recordsLinked to original sources

Vogls: a Fast Interactive Full-timing Simulator for Pre-silicon Power Side-Channel Analysis

Designing hardware circuits resistant to side-channel attacks increasingly relies on simulation to predict device leakage before fabrication. Current functional verification simulators are designed for extended correctness-checking runs and are ill-suited for producing large numbers of short trace collections with slight input variants needed for side-channel analysis. We present Vogls: an open-source Verilog simulator built for side-channel analysis, that is the first simulator to combine compiled-code performance, full-timing simulation, and fine-grained control over the simulation state. Vogls simulates a timing annotated gate-level AES design 5.9 times faster than Icarus Verilog and is only 30% slower than Verilator on a PicoRV32 RTL design while also offering a more accurate timing model. Vogls provides a Python interface that allows simulations to be paused, forked, inspected, and mutated around points-of-interest without modifying the hardware design and thereby preserving timing and leakage characteristics. Furthermore, a trace-collection workflow runs setup code once and forks at the point-of-interest, eliminating the need to re-simulate from reset for every trace. A case study demonstrates Vogls on a differential power analysis attack that successfully recovers the key at RTL, GTL and full-timing GTL abstraction levels.

cs.CR

Playing with blocks: Toward re-usable deep learning models for side-channel profiled attacks

This paper introduces a deep learning modular network for side-channel analysis. Our deep learning approach features the capability to exchange part of it (modules) with others networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures for each evaluation, reducing the body of work when conducting those. Our experiments demonstrate that our architecture feasibly assesses a side-channel evaluation suggesting that learning transferability is possible with the network we propose in this paper.

cs.CR

Being Patient and Persistent: Optimizing An Early Stopping Strategy for Deep Learning in Profiled Attacks

The absence of an algorithm that effectively monitors deep learning models used in side-channel attacks increases the difficulty of evaluation. If the attack is unsuccessful, the question is if we are dealing with a resistant implementation or a faulty model. We propose an early stopping algorithm that reliably recognizes the model's optimal state during training. The novelty of our solution is an efficient implementation of guessing entropy estimation. Additionally, we formalize two conditions, persistence and patience, for a deep learning model to be optimal. As a result, the model converges with fewer traces.

cs.CR

SoK: Design Tools for Side-Channel-Aware Implementations

Side-channel attacks that leak sensitive information through a computing device's interaction with its physical environment have proven to be a severe threat to devices' security, particularly when adversaries have unfettered physical access to the device. Traditional approaches for leakage detection measure the physical properties of the device. Hence, they cannot be used during the design process and fail to provide root cause analysis. An alternative approach that is gaining traction is to automate leakage detection by modeling the device. The demand to understand the scope, benefits, and limitations of the proposed tools intensifies with the increase in the number of proposals. In this SoK, we classify approaches to automated leakage detection based on the model's source of truth. We classify the existing tools on two main parameters: whether the model includes measurements from a concrete device and the abstraction level of the device specification used for constructing the model. We survey the proposed tools to determine the current knowledge level across the domain and identify open problems. In particular, we highlight the absence of evaluation methodologies and metrics that would compare proposals' effectiveness from across the domain. We believe that our results help practitioners who want to use automated leakage detection and researchers interested in advancing the knowledge and improving automated leakage detection.

cs.CR