Machine Learning-Assisted Side-Channel Analysis for Software Integrity Verification
2025 (English)In: 2025 IEEE European Test Symposium, ETS 2025, Institute of Electrical and Electronics Engineers (IEEE), 2025Conference paper, Published paper (Refereed)
Abstract [en]
Traditional cryptographic methods for software integrity verification rely on validating cryptographic signatures attached to software binaries. However, these methods primarily focus on load-time measurements and may be circumvented by an attacker interfering with the boot process. To address this limitation, we propose a novel approach that uses side-channel data collected during software execution to generate a proof of software integrity. Through a side-channel trace encoder, we generate cryptographic keys derived from the unique side-channel profiles of software processes. This ensures that only the processes with expected side-channel characteristics can produce the valid key, effectively linking software integrity verification to runtime behavior. We demonstrate the feasibility of this approach in a secure boot setting compliant with the TCG DICE framework. The presented solution provides holistic boot protection while enhancing resilience against attacks such as fault injection, misconfiguration, and downgrading of security algorithms.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2025.
Series
Proceedings of the European Test Symposium, ISSN 1530-1877
Keywords [en]
Monitoring, machine learning, secure boot, security, side-channel analysis
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-374670DOI: 10.1109/ETS63895.2025.11049653ISI: 001540479400053Scopus ID: 2-s2.0-105011078897OAI: oai:DiVA.org:kth-374670DiVA, id: diva2:2026147
Conference
2025 European Test Symposium-ETS-Annual, MAY 26-30, 2025, Tallinn, Estonia
Note
Part of ISBN 979-8-3315-9451-0; 979-8-3315-9450-3
QC 20260108
2026-01-082026-01-082026-01-08Bibliographically approved