Analysis and Improvement of the Components of SNOW 2.0 Based on Statistical Tests

The SNOW family is a main trend of the design of the stream cipher. Because of the security vulnerabilities of the SNOW family, this paper selects SNOW 2.0 algorithm which is the most representative of the family as a research object. Three core components of SNOW 2.0 that are mold addition on more than one domain, nonlinear S-box and Linear Feedback Shift Register (LFSR) are analyzed using statistical tests. Several improved algorithms are proposed based on improving random S-box and improving high performance LFSR. The result enhances effectively the security and performance of SNOW family.