CARNYX: A framework for vulnerability detection via power consumption analysis in embedded systems CARNYX: A framework for vulnerability detection via power consumption analysis in embedded systems

• Published in: International journal of information security Vol. 24; no. 4; p. 172
• Main Authors: Barredo, Jorge, Eceiza, Maialen, Flores, Jose Luis, Iturbe, Mikel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2025; Springer Nature B.V
• Subjects: Access control; Automation; Coding and Information Theory; Communications Engineering; Computer Communication Networks; Computer Science; Constraints; Cryptology; Cybersecurity; Embedded systems; Ethernet; Internet of Things; Management of Computing and Information Systems; Networks; Operating Systems; Power consumption; Power management; Regular Contribution; Serial interfaces; Software; Software reliability; Taxonomy; Embedded systems; Side-channel analysis; Hardware security; Vulnerability detection
• ISSN: 1615-5262; 1615-5270
• DOI: 10.1007/s10207-025-01092-2

The widespread use of the Internet of Things (IoT) has led to a surge in interconnected, resource-constrained embedded systems, which are inherently vulnerable due to limited security mechanisms. This paper presents CARNYX, a framework leveraging power consumption analysis, rooted in Side-Channel Analysis (SCA), to detect vulnerabilities in embedded systems with high accuracy. Designed for pre-deployment vulnerability detection, it offers three key advantages over existing SCA solutions: (1) detailed categorisation of specific vulnerability types beyond binary detection, (2) a methodology validated on the STM32F4 architecture and ARM Cortex-A8 with potential applicability to similar low- and medium-end systems, and (3) reliable detection in resource-constrained devices where power monitoring is practical. We evaluate CARNYX on three platforms: two low-end STM32F4-based platforms (Riscure Piñata and STM NUCLEO-144) and the medium-end ARM Cortex-A8-based BeagleBone Black, analysing 16 arithmetic and memory-related software flaws. Results demonstrate recall rates of 99.69% (Piñata), 86.88% (NUCLEO-144 with serial interface), 51.25% (NUCLEO-144 with Ethernet), and 53.67% (BeagleBone Black)-all with high precision-while measuring the effect of communication peripherals on side-channel leakage, an aspect underexplored in prior vulnerability detection studies. These results highlight CARNYX’s potential to enhance security in constrained IoT devices, even in noisy environments where binary detection methods offer limited value. While validated on STM32F4 and ARM Cortex-A8, its principles may extend to other low- and medium-end systems, subject to further validation.