In this paper, we conduct a detailed power side-channel analysis of Xoodyak, a lightweight cryptographic algorithm, on an FPGA platform. We focus on the initialization phase of Xoodyak in the authenticated encryption with associated data (AEAD) mode. First, we introduce a new leakage model and perform a leakage assessment. Then, we perform non-profiled and profiled attacks to determine if the observed leakages can be exploited. For a non-profiled attack, we perform a correlation power analysis on all key bits, achieving a success rate of 91.4% with 50 000 traces. Our approach for a profiled attack involves a template attack and a deep learning-based attack. The former achieves a success rate of 99.2%, recovering almost all key bits with 20 000 traces in the attack phase. The latter reaches a guessing entropy of zero after 550 traces and adapts to the leakage model within 50 epochs.

The efficiency of the profiling side-channel analysis can be significantly improved with machine learning techniques. Although powerful, a fundamental machine learning limitation of being data-hungry received little attention in the side-channel community. In practice, the maximum number of leakage traces that evaluators/attackers can obtain is constrained by the scheme requirements or the limited accessibility of the target. Even worse, various countermeasures in modern devices increase the conditions on the profiling size to break the target. This work demonstrates a practical approach to dealing with the lack of profiling traces. Instead of learning from a one-hot encoded label, transferring the labels to their distribution can significantly speed up the convergence of guessing entropy. By studying the relationship between all possible key candidates, we propose a new metric, denoted Label Correlation (LC), to evaluate the generalization ability of the profiling model. We validate LC with two common use cases: early stopping and network architecture search, and the results indicate its superior performance.

With the matter of secure communication between devices, and especially for IoT devices, more and more applications need trustful protocols to communicate using public key cryptography. Elliptic curve cryptography is nowadays a very secure and efficient public key cryptography method. One of the most recent and secure curve is Curve25519 and one of its failure is attack on low-order elements during a Diffie-Hellman key exchange. This document demonstrates that an attack using an order 4 point is possible on an embedded system with a simple power analysis, pointing out every IoT using Curve255119 as a cryptographic method, a potential target to side-channel attacks.