학술논문
基于人工智能的工程监理信息异常检测仿真技术研究 / Research on anomaly detection and simulation technology of engineering supervision information based on artificial intelligence
Document Type
Academic Journal
Author
Source
粘接 / Adhesion. 51(2):171-174
Subject
Language
Chinese
ISSN
1001-5922
Abstract
为降低工程监理信息系统异构感知网络的入侵攻击,建立了工程监理信息系统入侵检测模型,并将聚类协议与人工智能算法相结合,以提高系统入侵检测效率.实验结果表明,人工智能算法的准确率、F1 分数、召回率均大于 99.5%,可有效检测到工程监理数据信息异常获取,拒绝服务类攻击及病毒类攻击,从而保证工程监理信息系统运行正常.当误报率为0 时,基于人工智能算法的入侵检测率高达 97.3%;当误报率为 100%,入侵检测率仍高达91.2%.研究结果可为工程监理信息系统检测提供参考依据.
In order to reduce the intrusion attack of the heterogeneous perception network of the engineering supervi-sion information system,an intrusion detection model of the engineering supervision information system was estab-lished,and the clustering protocol and artificial intelligence algorithm were combined to improve the intrusion detec-tion efficiency of the system,The experimental results showed that the accuracy,F1 score and recall rate of artificial intelligence algorithms were all greater than 99.5%,which could effectively detect abnormal acquisition of engineer-ing supervision data information,denial of service attacks,and virus attacks,thereby ensuring the normal operation of the engineering supervision information system,When the false alarm rate was 0,the intrusion detection rate based on artificial intelligence algorithms was as high as 97.3%,and when the false alarm rate was 100%,the in-trusion detection rate was still as high as 91.2%,The research results can provide a reference basis for the detection of engineering supervision information systems.
In order to reduce the intrusion attack of the heterogeneous perception network of the engineering supervi-sion information system,an intrusion detection model of the engineering supervision information system was estab-lished,and the clustering protocol and artificial intelligence algorithm were combined to improve the intrusion detec-tion efficiency of the system,The experimental results showed that the accuracy,F1 score and recall rate of artificial intelligence algorithms were all greater than 99.5%,which could effectively detect abnormal acquisition of engineer-ing supervision data information,denial of service attacks,and virus attacks,thereby ensuring the normal operation of the engineering supervision information system,When the false alarm rate was 0,the intrusion detection rate based on artificial intelligence algorithms was as high as 97.3%,and when the false alarm rate was 100%,the in-trusion detection rate was still as high as 91.2%,The research results can provide a reference basis for the detection of engineering supervision information systems.