# SoK: Design Tools for Side-Channel-Aware Implementations

arXiv: https://arxiv.org/abs/2104.08593 · HTML: https://mlsanity.com/paper/2104.08593/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2021.

## Built on by (most cited first, 3 of 3)

- [A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation](https://mlsanity.com/paper/2204.09579/) (2022)
- [Securing Optimized Code Against Power Side Channels](https://mlsanity.com/paper/2207.02614/) (2022)
- [ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search](https://mlsanity.com/paper/2406.19549/) (2024)

## Abstract (arXiv)

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.