Presentation
Using ML-Assisted Side-Channel Analysis in Simulation: A Case Study on AES-GCM
DescriptionThis work presents a machine‑learning‑assisted approach to pre‑silicon side‑channel analysis (SCA) for AES‑GCM cryptographic IPs. Traditional SCA methods rely on explicit leakage models and manual point‑of‑interest selection, which can limit accuracy and scalability in early‑stage hardware security verification. In contrast, this study utilizes RTL‑generated power traces and profiling attacks powered by convolutional and multilayer perceptron models to automatically learn secret‑dependent leakage without prior assumptions. The proposed flow integrates RTL simulation, power‑trace preprocessing, and ML‑based key‑byte classification to evaluate leakage resilience before tape‑out. Experimental results using 10,000 simulated traces demonstrate successful key‑recovery performance, with a significant portion of AES‑GCM key bytes identified at low ranking positions across both ML models. These findings show that ML‑assisted SCA strengthens shift‑left security validation by revealing vulnerabilities early in the design cycle and reducing dependence on traditional leakage modeling techniques.
Event Type
Engineering Presentation
TimeWednesday, July 2911:45am - 12:00pm PDT
LocationSeaside Ballroom B