大规模数据库高危攻击数据实时挖掘仿真研究
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摘要
对大规模数据库的高危攻击数据进行挖掘,能有效提高数据挖掘的精度,提高数据库防攻击的性能。当前利用关联规则的映射挖掘算法,对攻击数据进行挖掘时,由于数据较多,数据挖掘的准确度较低,降低了高危数据挖掘的精度。提出基于粒子群优化的攻击数据检测的算法。利用粗糙集的理论对大规模数据库高危攻击的数据进行属性的约简,提高攻击数据属性的依赖度,利用粒子群优化检测算法对大规模数据库高危的攻击数据进行检测,针对粒子群算法存在局部的早熟收敛的问题,采用改进粒子的属性,对粒子群算法进行改进,增加粒子的多样性,经过种群的初始化以速度与位置进行的更新,对粒子适应度的值进行计算,对粒子全局的极值进行更新,进行粒子循环的迭代,得出最优的解,完成对大规模数据库的高危攻击的数据实时的挖掘。实验的结果表明,利用所提的算法,在减少内存的占用容量的同时,有效地提高了数据实时挖掘的精度。
An algorithm for detecting attack data based on particle swarm optimization was put forward. Firstly,this research used the theory of rough set to carry out attribute reduction of high-risk attack data in large-scale database and improve the degree of dependence for attack data attribute. Then,our research used particle swarm optimization algorithm to detect high-risk attack data in large-scale database. For problem of local premature convergence of particle swarm optimization,the research used the attribute of improved particle to improve the particle swarm algorithm and increase the diversity of particles. Through the initialization of population and the updating of speed and position,the research calculated particle fitness values and update global extreme value of particle. Finally,the iteration of particle recycling was carried out to find the optimal solution. Thus,we completed the real-time mining for high-risk attack data in large-scale database. Simulation results prove that the proposed algorithm can improve the accuracy of real-time data mining when reducing the amount of memory footprint.
An algorithm for detecting attack data based on particle swarm optimization was put forward. Firstly,this research used the theory of rough set to carry out attribute reduction of high-risk attack data in large-scale database and improve the degree of dependence for attack data attribute. Then,our research used particle swarm optimization algorithm to detect high-risk attack data in large-scale database. For problem of local premature convergence of particle swarm optimization,the research used the attribute of improved particle to improve the particle swarm algorithm and increase the diversity of particles. Through the initialization of population and the updating of speed and position,the research calculated particle fitness values and update global extreme value of particle. Finally,the iteration of particle recycling was carried out to find the optimal solution. Thus,we completed the real-time mining for high-risk attack data in large-scale database. Simulation results prove that the proposed algorithm can improve the accuracy of real-time data mining when reducing the amount of memory footprint.
引文
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[2]侯燕,李巍,文乔农.基于复杂网络数据流密度的增量子空间数据挖掘算法[J].计算机应用研究,2015,32(7):2023-2026.
[3]张燕.数据挖掘提取查询树特征的SQL注入攻击检测[J].电子技术应用,2016,42(3):90-94.
[4]徐开勇,龚雪容,成茂才.基于改进Apriori算法的审计日志关联规则挖掘[J].计算机应用,2016,36(7):1847-1851.
[5]张新英,付川南.一种高效的多类型数据挖掘算法[J].中国电子科学研究院学报,2017,12(4):359-364.
[6]唐晓东.基于关联规则映射的生物信息网络多维数据挖掘算法[J].计算机应用研究,2015,32(6):1614-1616.
[7]张继福,等.基于MapReduce与相关子空间的局部离群数据挖掘算法[J].软件学报,2015,26(5):1079-1095.
[8]赵小强,张露.基于改进FSVM的数据挖掘分类算法[J].兰州理工大学学报,2016,42(2):101-106.
[9]金涛,等.基于Android终端与数据挖掘的FMIPv6切换算法[J].计算机应用研究,2016,33(4):1224-1227.
[10]董本清,彭健钧.复杂网络数据流中的异常数据挖掘算法仿真[J].计算机仿真,2016,33(1):434-437.