A Random Forest trained on timing and ciphertext features can detect self-injected timing and bit-flip anomalies in AES-128 better than a timing threshold, on CPU and PYNQ-Z1.
Pynq-z1: Python productivity for zynq-7000 arm/fpga soc,
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ML-Enhanced AES Anomaly Detection for Real-Time Embedded Security
A Random Forest trained on timing and ciphertext features can detect self-injected timing and bit-flip anomalies in AES-128 better than a timing threshold, on CPU and PYNQ-Z1.