摘要
传统的入侵数据挖掘方法对船舶无线传感网络内的高危入侵数据行为感知不强,无法完整提取高危入侵数据的特征链,极大地影响挖掘效果。因此,提出基于云平台的船舶无线传感网络高危入侵数据挖掘方法。提出方法主要对无线传感网络内高危入侵数据检测算法进行优化,提升对高危入侵数据的感知;根据感知数据提取入侵数据的行为特征;再根据特征数据建立高危入侵特征样本挖掘学习机,完成对船舶无线传感网络高危入侵数据的挖掘计算。为了验证提出方法的挖掘效果,设计采用仿真实验的方式,通过模拟数据的对比,得出有效性结论。
The traditional intrusion data mining method does not perceive the high risk intrusion data behavior in the ship wireless sensor network, and can not extract the feature chain of the high risk intrusion data completely, which greatly affects the mining effect. Therefore, the high risk intrusion data mining method of ship wireless sensor network based on cloud platform is proposed. The proposed method mainly optimizes the high risk intrusion data detection algorithm in wireless sensor network, improves the perception of high risk intrusion data, extracts the behavior characteristics of intrusion data according to the perceptual data, and establishes the high risk intrusion feature sample mining learning machine according to the feature data to complete the mining calculation of high risk intrusion data in ship wireless sensor network. For The mining effect of the proposed method is verified, and the simulation experiment is, and the validity conclusion is obtained by comparing the simulation data.
作者
宋俊苏
SONG Jun-su(Yancheng Biological Engineering Branch Institute,Jiangsu Union Technical Institute,Yancheng 224051,China)
出处
《舰船科学技术》
北大核心
2020年第24期124-126,共3页
Ship Science and Technology
基金
江苏省职业教育教学改革研究项目(ZYB136)
关键词
云平台
高危入侵
数据
挖掘
cloud platform
high risk intrusion
data
mining
