EM-X-DL: Efficient Cross-device Deep Learning Side-channel Attack With Noisy EM Signatures

This work presents a Cross-device Deep-Learning based Electromagnetic (EM-X-DL) side-channel analysis (SCA) on AES-128, in the presence of a significantly lower signal-to-noise ratio (SNR) compared to previous works. Using a novel algorithm to intelligently select multiple training devices and proper choice of hyperparameters, the proposed 256-class deep neural network (DNN) can be trained efficiently utilizing pre-processing techniques like PCA, LDA, and FFT on measurements from the target encryption engine running on an 8-bit Atmel microcontroller. In this way, EM-X-DL achieves >90% single-trace attack accuracy. Finally, an efficient end-to-end SCA leakage detection and attack framework using EM-X-DL demonstrates high confidence of an attacker with <20 averaged EM traces.

Countermeasures against physical security attacks on ICs utilizing on-chip wideband ADCs

Cryptographic ICs on edge devices for internet-of-things (IoT) applications are exposed to an adversary and threatened by malicious side channel analysis. On-chip analog monitoring by sensor circuits embedded inside the chips is one of the possible countermeasures against such attacks. An on-chip monitor circuit consisting of a successive approximation register (SAR) analog-to-digital converter (ADC) and an input buffer acquires a wideband signal, which enables to detects an irregular noise due to an active fault injection and a passive side channel leakage analysis. In this paper, several countermeasures against security attacks utilizing wideband on-chip monitors are reviewed. Each technique is implemented on a prototype chip, and the measurement results prove they can effectively detect and diagnose the security attacks.