One for All and All for One: GNN-based Control-Flow Attestation for
  Embedded Devices
Marco Chilese Richard Mitev Meni Orenbach Robert Thorburn Ahmad Atamli Ahmad-Reza Sadeghi
Control-Flow Attestation (CFA) is a security service that allows an entity
(verifier) to verify the integrity of code execution on a remote computer
system (prover). Existing CFA schemes suffer from impractical assumptions, such
as requiring access to the prover's internal state (e.g., memory or code), the
complete Control-Flow Graph (CFG) of the prover's software, large sets of
measurements, or tailor-made hardware. Moreover, current CFA schemes are
inadequate for attesting embedded systems due to their high computational
overhead and resource usage.
  In this paper, we overcome the limitations of existing CFA schemes for
embedded devices by introducing RAGE, a novel, lightweight CFA approach with
minimal requirements. RAGE can detect Code Reuse Attacks (CRA), including
control- and non-control-data attacks. It efficiently extracts features from
one execution trace and leverages Unsupervised Graph Neural Networks (GNNs) to
identify deviations from benign executions. The core intuition behind RAGE is
to exploit the correspondence between execution trace, execution graph, and
execution embeddings to eliminate the unrealistic requirement of having access
to a complete CFG.
  We evaluate RAGE on embedded benchmarks and demonstrate that (i) it detects
40 real-world attacks on embedded software; (ii) Further, we stress our scheme
with synthetic return-oriented programming (ROP) and data-oriented programming
(DOP) attacks on the real-world embedded software benchmark Embench, achieving
98.03% (ROP) and 91.01% (DOP) F1-Score while maintaining a low False Positive
Rate of 3.19%; (iii) Additionally, we evaluate RAGE on OpenSSL, used by
millions of devices and achieve 97.49% and 84.42% F1-Score for ROP and DOP
attack detection, with an FPR of 5.47%.