基于大数据的半分布式僵尸网络动态抑制算法

为了提高半分布式僵尸网络的安全性，提出一种基于大数据的半分布式僵尸网络动态抑制算法。采用波特间隔均衡控制方法，进行半分布式僵尸网络的动态特征补偿，构建半分布式僵尸网络动态特征信息采样模型，利用判决均衡方法，对采集到的动态特征信息进行定量递归分析，提取半分布式僵尸网络的统计特征量；在此基础上，采用大数据寻优计算方法，获取真正的半分布式僵尸网络最优抑制参数，在嵌入式环境下，将半分布式僵尸网络的最优抑制参数与迁移负载响应结果相结合，实现半分布式僵尸网络动态抑制。仿真结果表明，采用本文方法进行半分布式僵尸网络动态抑制的效果较好，提高了抑制精度，缩短了抑制时间，且降低了网络输出误码率。

Dynamic Suppression Algorithm of Semi-distributed Zombie Network Based on Big Data

In order to improve the security of semi-distributed botnet, a dynamic suppression algorithm of semi-distributed botnet based on big data is proposed. The method of baud interval equalization control is used to compensate the dynamic characteristics of the semi-distributed botnet. The sampling model of the dynamic characteristics of the semi-distributed botnet is constructed. The decision equalization method is used to analyze the collected dynamic characteristics quantitatively and recursively and extract the statistical characteristics of the semi-distributed botnet. On this basis, the big data optimization calculation method is used to achieve the true optimal suppression of the semi-distributed botnet. Under the embedded environment, the optimal inhibition parameters of the semi-distributed botnet are combined with the results of load response to realize the dynamic inhibition of the semi-distributed botnet. The simulation results show that the semi-distributed botnet dynamic suppression method has a good effect, it improves the suppression precision, shortens the suppression time, and reduces the bit error rate of network output.