Paper 2024/1214

Less Effort, More Success: Efficient Genetic Algorithm-Based Framework for Side-channel Collision Attacks

Abstract

By introducing collision information, the existing side-channel Correlation-Enhanced Collision Attacks (CECAs) performed collision-chain detection, and reduced a given candidate space to a significantly smaller collision-chain space, leading to more efficient key recovery. However, they are still limited by low collision detection speed and low success rate of key recovery. To address these issues, we first give a Collision Detection framework with Genetic Algorithm (CDGA), which exploits Genetic Algorithm to detect the collision chains and has a strong capability of global searching. Secondly, we theoretically analyze the performance of CECA, and bound the searching depth of its output candidate vectors with a confidence level using a rigorous hypothesis test, which is suitable both for Gaussian and non-Gaussian leakages. This facilitates the initialization of the population. Thirdly, we design an innovative goal-directed mutation method to randomly select new gene values for replacement, thus improving efficiency and adaptability of the CDGA. Finally, to optimize the evolutionary of CDGA, we introduce roulette selection strategy to employ a probability assignment based on individual fitness values to guarantee the preferential selection of superior genes. A single-point crossover strategy is also used to introduce novel gene segments into the chromosomes, thus enhancing the genetic diversity of the population. Experiments verify the superiority of our CDGA.