摘要
针对数据不平衡导致的分类器偏向问题,该文提出面向安全生产环境监控网络的安全态势感知方法。采集了监控网络的相关安全数据,通过朴素贝叶斯算法提取了关键信息。通过属性加权算法对贝叶斯网络进行改进,量化了监控网络安全态势评估结果,判断当前的安全状态、潜在威胁和可能存在的风险。综合评估结果,得到安全态势感知结果。结果表明,当时间为40 s时,该文方法的网络安全态势感知值为94,与实际结果一致,能够及时发现安全隐患。
Regarding the issue of classifier bias caused by imbalanced data.The paper proposes a security situational awareness method for safety production environment monitoring networks.We collected security related data from the monitoring network and extracted key information using the Naive Bayes algorithm.By improving the Bayesian network through attribute value addition algorithm,the results of monitoring network security situation assessment were quantified to determine the current security status,potential threats,and potential risks.Based on the comprehensive evaluation results,obtain the security situation awareness results.The results show that when the time is 40 seconds,the network security situational awareness value of our method is 94,which is consistent with the actual results and can detect security risks in a timely manner.
作者
郭娴
鞠远
刚占慧
樊佳讯
GUO Xian;JU Yuan;GANG Zhanhui;FAN Jiaxun(China Industrial Control Systems Cyber Emergency Response Team,Beijing 100040,China)
出处
《电子设计工程》
2026年第4期17-20,25,共5页
Electronic Design Engineering
关键词
安全生产环境
监控网络
安全态势感知
贝叶斯网络模型
态势提取
态势评估
safety production environment
monitoring network
security situation awareness
Bayesian network model
situation extraction
situation assessment
