基于DS证据理论的多源网络安全数据融合模型OACSTPCD

Network security situation awareness involves a large amount of multi-source data,and its information extraction is difficult,which is an urgent problem to be solved at present.In combination with the existing network security practices,a multi-source network security data fusion method based on the Dempster-Shafer(DS)evidence theory is studied specifically for data generated by flow sensors.By designing an effective fusion model,this method reduces data redundancy,implement correlation analysis,and analyze the correlation between network security events from the dimensions of time,space and events,so as to form fused data with correlations,and thereby enhance the effectiveness of network security situation data.The proposed fusion model effectively extracts key information,enhances the validity of network security situation data,and provides strong support for network security regulation.Furthermore,it maintains a high level of effectiveness even in the presence of possible false positives or missed detections,so it demonstrates its significant practical application value and promotional significance.