船用SDN传输网非法入侵行为的识别研究  

Research on identification of illegal intrusion in marine SDN transmission network

作　　者：张文娟[1] 金会赏 薛金召[2] ZHANG Wen-juan;JIN Hui-shang;XUE Jin-zhao(Cangzhou Technical College,Cangzhou 061001,China;Hunan Petrochemical Vocational Technology College,Yueyang 414012,China)

机构地区：[1]沧州职业技术学院,河北沧州061001 [2]湖南石油化工职业技术学院,湖南岳阳414012

出　　处：《舰船科学技术》2021年第14期175-177,共3页Ship Science and Technology

基　　金：湖南省教育厅科学研究项目(19C1185)

摘　　要：由于非法入侵行为产生的数据较多,传统方法受到数据冗余性较高的影响,导致识别准确性较低,为此设计一个船用SDN传输网非法入侵行为的识别方法。首先对传输网中的数据进行降维,然后对网络中的冗余数据去除,寻找数据之间的频繁项集,并对数据之间的关联规则生成,以对数据关联性计算。最后采用粗糙集算法对异常行为识别,主要通过奇异值计算与阈值比较实现。实验以识别准确率与识别时间作为对比对象,结果表明所研究的识别方法有效提高了识别的准确率,并减少了识别时间,实际应用意义较强。Due to the large amount of data generated by illegal intrusion,the traditional methods are affected by the high redundancy of data,resulting in the low accuracy of identification.Therefore,a method for identifying illegal intrusion in marine SDN transmission network is designed.Firstly,the dimension of the data in the transmission network is reduced,then the redundant data in the network is removed,and then the frequent itemsets between the data are found,and the association rules between the data are generated to calculate the data association.Finally,the rough set algorithm is used to identify the abnormal behavior,which is mainly realized by singular value calculation and threshold comparison.The experimental results show that the proposed method can effectively improve the recognition accuracy and reduce the recognition time.

关 键 词：传输网 非法入侵 识别方法 准确率 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]