{"title":"计算机网络监控的多智能体方法","authors":"B. Assanovich, Alexey Ivanov, Vadim Savenkov","doi":"10.1109/ICISCT55600.2022.10146762","DOIUrl":null,"url":null,"abstract":"A greedy modified allocation algorithm for the computer network monitoring based on iterative maximization of path coverage in network graph is developed and a multiagent approach for the implementation of network analysis is presented. Experiments have demonstrated an improvement in the performance of network monitoring when agents are trained, interact and coordinate.","PeriodicalId":332984,"journal":{"name":"2022 International Conference on Information Science and Communications Technologies (ICISCT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-Agent Approach for Monitoring of Computer Networks\",\"authors\":\"B. Assanovich, Alexey Ivanov, Vadim Savenkov\",\"doi\":\"10.1109/ICISCT55600.2022.10146762\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A greedy modified allocation algorithm for the computer network monitoring based on iterative maximization of path coverage in network graph is developed and a multiagent approach for the implementation of network analysis is presented. Experiments have demonstrated an improvement in the performance of network monitoring when agents are trained, interact and coordinate.\",\"PeriodicalId\":332984,\"journal\":{\"name\":\"2022 International Conference on Information Science and Communications Technologies (ICISCT)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Information Science and Communications Technologies (ICISCT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISCT55600.2022.10146762\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Information Science and Communications Technologies (ICISCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISCT55600.2022.10146762","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-Agent Approach for Monitoring of Computer Networks
A greedy modified allocation algorithm for the computer network monitoring based on iterative maximization of path coverage in network graph is developed and a multiagent approach for the implementation of network analysis is presented. Experiments have demonstrated an improvement in the performance of network monitoring when agents are trained, interact and coordinate.
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