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An Improvement of Linearization-Based Algebraic Attacks

机译:基于线性化的代数攻击的一种改进

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In an algebraic attack on a cipher, one expresses the encryption function as a system (usually overdefined) of multivariate polynomial equations in the bits of the plaintext, the ciphertext and the key, and subsequently solves the system for the unknown key bits from the knowledge of one or more plaintext/ciphertext pairs. The standard extended Linearization algorithm (XL) expands the initial system of equations by monomial multiplications. The expanded system is treated as a linear system in the monomials. For most block ciphers (like the Advanced Encryption Standard (AES)), the size of the linearized system turns out to be very large, and consequently, the complexity to solve the system often exceeds the complexity of brute-force search. In this paper, we propose a heuristic strategy XL_SGE to reduce the number of linearized equations. This reduction is achieved by applying structured Gaussian elimination before each stage of monomial multiplication. Experimentation on small random systems indicates that XL_SGE has the potential to improve the performance of the XL algorithm in terms of the size of the final solvable system. This performance gain is exhibited by our heuristic also in the case of a toy version of AES.
机译:在对密码的代数攻击中,人们将加密函数表示为一个系统的(通常是超定义的)明文,密文和密钥中的多元多项式方程组,然后从知识中解出未知密钥位的系统一个或多个明文/密文对。标准扩展线性化算法(XL)通过单项乘法扩展了方程的初始系统。在单项式中,扩展系统被视为线性系统。对于大多数分组密码(例如高级加密标准(AES)),线性化系统的大小非常大,因此解决该系统的复杂性通常超过了蛮力搜索的复杂性。在本文中,我们提出了一种启发式策略XL_SGE来减少线性化方程的数量。通过在单项式乘法的每个阶段之前应用结构化的高斯消除来实现此减少。在小型随机系统上进行的实验表明,XL_SGE有潜力根据最终可解决系统的大小来提高XL算法的性能。在AES玩具版本的情况下,我们的启发式方法也显示了这种性能提升。

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