CAJ | 학술논문

针对高维数入侵检测数据集中信息冗余导致入侵检测算法处理速度慢的问题,提出了一种基于粒子群优化的入侵特征选择算法,通过分析网络入侵数据特征之间的相关性,可使粒子群优化算法在所有特征空间中优化搜索,自主选择有效特征子集,降低数据维度.实验结果表明该算法能够有效去除冗余特征,减少特征选择时间,在保证检测准确率的前提下,有效地提高了系统的检测速度.
침대고유수입침검측수거집중신식용여도치입침검측산법처리속도만적문제,제출료일충기우입자군우화적입침특정선택산법,통과분석망락입침수거특정지간적상관성,가사입자군우화산법재소유특정공간중우화수색,자주선택유효특정자집,강저수거유도.실험결과표명해산법능구유효거제용여특정,감소특정선택시간,재보증검측준학솔적전제하,유효지제고료계통적검측속도.
@@@@Intrusion feature selection can improve the correctness and detection rate of the intrusion detection system effectively. A intrusion feature subset selection algorithm based on Particle Swarm Optimization(PSO)is proposed. Depending on the analyses of correlation between all features of network intrusion data, the PSO algorithm is used to search and choose effective feature subset independently to reduce data dimension in the feature space. The experimental results show that the algorithm can effec-tively remove redundant features, reduce the time of feature selection, ensure detection accuracy and improve detecting speed.