Beyond TVLA: Anderson-Darling Leakage Assessment for Neural Network Side-Channel Leakage Detection
This work introduces Anderson–Darling Leakage Assessment (ADLA) to detect side-channel leakage in neural networks. Unlike standard Test Vector Leakage Assessment (TVLA), ADLA compares full cumulative distribution functions rather than just the mean. Experiments show ADLA is more sensitive than TVLA at low trace counts, detecting leakage through broader distributional differences.