摘要
为了有效发现大数据网络中潜在的安全风险,文章提出了基于改进决策树算法的大数据网络信息安全态势感知方法。该方法通过分析大数据网络信息安全态势感知架构,明确感知原理结构;在数据采集阶段,用均值插值识别无效流量,提取初始感知数据;对初始感知数据进行流量分析,生成态势感知信号;应用基尼系数改进决策树算法,选择最佳属性,最终量化网络信息安全态势,实现有效感知。实验结果表明:利用设计方法所产生的感知结果准确率最高为97%、感知时间最小为51.65ms,可以有效实现大数据网络信息安全态势的准确感知,快速确定感知数据的威胁预警等级。
In order to effectively discover the potential security risks in big data network,this paper proposes a big data network information security situation awareness method based on improved decision tree algorithm.By analyzing the big data network information security situation awarcncss architecturc,this method defines the perccption principle structure;In the data acquisition phasc,the mean interpolation is used to identify the invalid traffic and extract the initial perception data;Analyze the flow of initial perception data to generate situation awareness signals;The Gini coefficient is applied to improve the decision tree algorithm,select the best attribute,and finally quantify the network information sccurity situation to realize effective perception.The experimental results show that:the highest accuracy of the perception results generated by the design method is 97%,and the minimum perception time is 51.65ms,which can effectively realize the accurate perception of the big data network information security situation,and quickly determine the threat warning level of the perceived data.
作者
温璇
WEN Xuan(Shanxi Universityof Applied Science and Technology,Taiyuan Shanxi 030062 China)
出处
《长江信息通信》
2024年第9期77-79,共3页
Changjiang Information & Communications
关键词
基尼系数
决策树算法
大数据网络
安全态势感知
Gini coefficient
decision tree algorithm
big data networks
security situational awareness
