摘要
网络和硬盘存储数据的有效恢复是进行网络刑事和经济侦查的一个重要的勘查技术。传统的数据恢复方法采用的是算术编码的数据恢复方法,随着干扰数据的增多,难以对有用数据实现准确恢复。提出一种基于相干函数无偏估计的数据恢复算法。构建存储介质中的数据结构模型,对待恢复的数据结构进行特征分解和信息模型构建,采用相干函数特征分解算法实现对待恢复数据的筛分和残差滤除,最后采用相干函数无偏估计算法实现数据的准确恢复,仿真实验进行了性能测试和验证。实验结果表明,采用该算法进行网络存储介质上的数据恢复,吞吐性能和准确度较传统方法高。
The effective recovery of network and hard disk storage data is an important exploration technology of network criminal and economic investigation. The traditional method of data recovery is the arithmetic encoding data recovery method, with the increase of interference data, it is difficult to achieve accurate recovery of useful data. A data recovery algorithm based on the unbiased estimation of the coherence function is proposed. To construct the data structure model of the storage medium, and to construct the data structure of recovery data structure, use the relevant functional decomposition algorithm to achieve the filtering and residual error. Finally, the performance of the algorithm is tested and verified. The results show that the performance and accuracy of the proposed algorithm is higher than the traditional method.
出处
《科技通报》
北大核心
2016年第3期105-108,184,共5页
Bulletin of Science and Technology
基金
2015年河南省高等学校重点科研项目(项目编号:15B520027)
关键词
数据恢复
相干函数
无偏估计
数据库
data recovery
coherence function
unbiased estimation
database
