Not So Greedy: Enhanced Subset Exploration for Nonrandomness Detectors

Distinguishers and nonrandomness detectors are used to distinguish ciphertext from random data. In this paper, we focus on the construction of such devices using the maximum degree monomial test. This requires the selection of certain subsets of key and IV-bits of the cipher, and since this selection to a great extent affects the final outcome, it is important to make a good selection. We present a new, generic and tunable algorithm to find such subsets. Our algorithm works on any stream cipher, and can easily be tuned to the desired computational complexity. We test our algorithm with both different input parameters and different ciphers, namely Grain-128a, Kreyvium and Grain-128. Compared to a previous greedy approach, our algorithm consistently provides better results.