交互式网络恶意入侵跳频数据特征自动挖掘方法  

Automatic mining method for frequency hopping data characteristics of interactive network malicious intrusion

作　　者：付吉菊 FU Jiju(School of Management and Information,Chuzhou city Career Academy,Chuzhou Anhui 239000)

机构地区：[1]滁州城市职业学院管理与信息学院,安徽滁州239000

出　　处：《宁夏师范学院学报》2022年第7期72-79,共8页Journal of Ningxia Normal University

基　　金：安徽省滁州城市职业学院学院重点教研项目(2020jyxm10).

摘　　要：交互式网络有容易被恶意入侵的缺点,为此提出交互式网络恶意入侵跳频数据特征自动挖掘方法研究.先在交互式网络中采集跳频数据样本,聚类处理了跳频数据,然后利用不同类型跳频数据之间的识别阈值,完成跳频数据的识别,再采用Apriori算法设计网络恶意入侵跳频数据分布算法,在交互式网络恶意入侵跳频数据的信源分布域中,检索出网络恶意入侵跳频数据的频繁项集,通过构建跳频数据特征挖掘模型,实现网络恶意入侵跳频数据特征的挖掘.最后实验结果表明,文中方法在挖掘跳频数据特征时,能够降低抗干扰系数和冗余度,保证了特征挖掘的性能和质量.Interactive networks have the disadvantage of being vulnerable to malicious intrusions.In order to solve this problem,an automatic mining method of frequency hopping data features of malicious intrusion in interactive networks is proposed.Firstly,the FH data samples are collected in the interactive network and processed by clustering.Seconclly,the identification of frequency hopping data is completed by using the identification threshold between different types of frequency hopping data.Thirdly,the network malicious intrusion frequency hopping data distribution algorithm is designed by using the Apriori algorithm to retrieve the frequent item set of network malicious intrusion frequency hopping data in the source distribution domain of interactive network malicious intrusion frequency hopping data.Finally,the feature mining model of frequency hopping data is constructed to realize the mining of frequency hopping data characteristics of network malicious intrusion.The experimental results show that the proposed method can reduce the anti-interference coefficient and redundancy,and ensure the performance and quality of feature mining.

关 键 词：特征挖掘 恶意入侵 交互式网络 跳频数据 数据采集 频繁项集 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]