提出了基于样本数据组合的Fuzzing技术,并抽象出了解决样本数据覆盖问题(data sample covering problem,DSCP)的数学模型;为了更好地解决样本数据覆盖问题,提出了改进遗传算法(developed genetic algorithm,DGA),通过实例实验说明了DGA的有效性,并且通过仿真实验,验证了算法在求解复杂协议文件样本数据覆盖问题时比贪心算法和简单遗传算法具有更高的效率。
This paper proposed the Fuzzing technology based on data sample combination and abstracted a mathematic model to solve DSCP. To solve DSCP problem,proposed DGA. The practical experiment results show the effectiveness of DGA. The simulated experiments show that the proposed DGA works more efficiently than the greedy algorithm and simple genetic algorithm.