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提出一种新的完美模糊彩虹表预计算和在线分析方法,在预计算表的生成与存储过程中,将存储空间分块并设置索引,依据预计算链终节点对应的索引值进行存储,并在这一过程中完成对合并链的处理。在线分析阶段,借助索引对查表区域进行精确定位,有效减少了外存储器访问次数,提高了在线分析阶段的查表效率。与原有完美模糊彩虹表方法的对比表明,在相同的攻击成功率要求下,本方法预计算阶段的存储空间约减率可达到18%以上;而在线分析阶段,若综合考虑单向函数迭代与外存储器访问所需时间,文章方法对长度<8的全可打印字符口令进行攻击所需的在线阶段时间仅为原有方法的7.6%。
In this paper, a new perfect fuzzy rainbow table pre-calculation and online analysis method are proposed. During the generation and storage of pre-calculation table, the storage space is divided into blocks and indexed, and stored according to the index value corresponding to the end node of pre-calculation chain. In the process to complete the merger chain processing. In the online analysis phase, the accurate positioning of the look-up table area by means of the index effectively reduces the number of external memory accesses and improves the efficiency of look-up table in the online analysis phase. Compared with the original perfect fuzzy rainbow table method, this method shows that the reduction rate of storage space in the pre-computation stage can reach more than 18% under the same requirement of attack success rate. In the online analysis stage, considering the one-way function iteration The article method’s attack time on all-printable characters with a length <8 required only an online phase of 7.6% of the time required for accessing the external memory.