船舶网络入侵信号取证过程破损数据恢复研究
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摘要
传统针对船舶入侵信号取证过程中,破损数据的恢复方法忽略了系数矩阵,导致数据恢复精确度较低。对此提出以船舶破损数据评估模型为核心的船舶破损数据恢复方法。将船舶破损数据所对应的对数函数作为模型建立所使用的目标函数,引入遗传算法,建立破损数据评估模型,评估破损数据并分别作为数据训练样本以及数据测试样本,建立系数矩阵,使用正向规则化因子范数作为惩罚目标系数,对系数矩阵进行稀疏化操作,完成数据恢复。实验数据表明,设计的破损数据恢复方法重删率提高27%,破损数据轻磁数提高19%,可以证明数据恢复效果精确度更高。
The traditional signals for vessels invasion forensics ignores damage in the process of data recovery coefficient matrix, leading to a lower accuracy of data recovery. This evaluation model based on the damage data of new data recovery method. To damage data logarithmic function as objective function, the genetic algorithm is introduced, corrupted data evaluation model is set up, after will assess the damage data samples, divided into training samples and testing samples,using the data of the training sample data, reconstruct the coefficient matrix, use positive norm regularization factors as punishment coefficient of target, ensure the reconstruction of sparse matrix, complete data recovery. Experimental data shows that the breakage of the design data recovery method delete ratio increased by 27%, damage data light magnetic number increased by 19%, and can prove that data recovery result accuracy is higher.
The traditional signals for vessels invasion forensics ignores damage in the process of data recovery coefficient matrix, leading to a lower accuracy of data recovery. This evaluation model based on the damage data of new data recovery method. To damage data logarithmic function as objective function, the genetic algorithm is introduced, corrupted data evaluation model is set up, after will assess the damage data samples, divided into training samples and testing samples,using the data of the training sample data, reconstruct the coefficient matrix, use positive norm regularization factors as punishment coefficient of target, ensure the reconstruction of sparse matrix, complete data recovery. Experimental data shows that the breakage of the design data recovery method delete ratio increased by 27%, damage data light magnetic number increased by 19%, and can prove that data recovery result accuracy is higher.
引文
[1]孟祥鹏.大数据网络恶意入侵数据准确恢复仿真研究[J].计算机仿真, 2017, 7(12):279–282.
[2]万超.数据恢复技术在数据恢复取证中的应用研究[J].福建电脑, 2017, 33(1):114–115.
[3]梁效宁,黄旭,赵飞.监控视频数据取证与恢复技术的研究[J].计算机科学, 2016, 43(b12):110–113.
[2]万超.数据恢复技术在数据恢复取证中的应用研究[J].福建电脑, 2017, 33(1):114–115.
[3]梁效宁,黄旭,赵飞.监控视频数据取证与恢复技术的研究[J].计算机科学, 2016, 43(b12):110–113.