网络存储隐蔽信道检测的改进与优化仿真

摘要

在网络存储隐蔽信道通过存储特征的异常性检测优化中,由于存储特征有较强的伪装性和变化随机性,采用当前算法进行隐蔽信道检测时,各种存储特征指标无法完成量化,存在隐蔽信道检测误差大的问题.为解决上述问题,提出一种改进校正熵的网络存储隐蔽信道优化检测方法.上述方法先引入高阶随机统计量描述网络用户存储规律,融合于条件熵理论,对网络用户存储操作序列的次数进行预测,并利用熵率的随机性度量指标衡量网络原始存储数据流熵率的变化,计算出网络存储数据变化的规律性指标,以上述指标为依据对网络存储隐蔽信道进行检测,改进了当前算法的弊端,极大地提升了算法的检测精度.仿真结果表明,上述算法提高了网络存储隐蔽信道检测精确度.%the network storage covert channel will endanger the safe and stable operation of network,so the need for network storage covert channel detection.But the current algorithm is adopted to improve the detecting covert channel can only detect a covert channel,great error in the presence of covert channel detection problem.For this,put forward an improved correction based on network storage covert channel optimization method of entropy.This method first introduced advanced random statistic description of network users to store rule,fusion in conditional entropy theo-ry to predict the number of network users to store operation sequence,on the basis of using the random metric based on entropy rate measuring network original storage data flow changes in the rate of entropy,calculate the network storage data change regularity index,based on the index to test the network storage covert channel,improve the current algorithm can only detect the defects in a covert channel,greatly improve the accuracy of the algorithm.Simulation experiment proves that the proposed algorithm network storage covert channel detection accuracy of optimization.