{"title":"分布式系统的僵尸网络检测方法","authors":"O. Savenko, A. Sachenko, S. Lysenko, G. Markowsky","doi":"10.1109/IDAACS.2019.8924428","DOIUrl":null,"url":null,"abstract":"This article presents the technique for botnet detection using the distributed systems in the local area network. Distributed system contains host and network levels. At the host level, the botnets detection is based on Bayes classification. In order to perform the classification, the classes and subclasses were constructed on the basis of botnets patterns. An algorithm for classifier training was developed. The network level provides the exchange of the classification results for the knowledge transfer to the rest of the antivirus program units of the distributed system.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Botnet Detection Approach for the Distributed Systems\",\"authors\":\"O. Savenko, A. Sachenko, S. Lysenko, G. Markowsky\",\"doi\":\"10.1109/IDAACS.2019.8924428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents the technique for botnet detection using the distributed systems in the local area network. Distributed system contains host and network levels. At the host level, the botnets detection is based on Bayes classification. In order to perform the classification, the classes and subclasses were constructed on the basis of botnets patterns. An algorithm for classifier training was developed. The network level provides the exchange of the classification results for the knowledge transfer to the rest of the antivirus program units of the distributed system.\",\"PeriodicalId\":415006,\"journal\":{\"name\":\"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IDAACS.2019.8924428\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IDAACS.2019.8924428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Botnet Detection Approach for the Distributed Systems
This article presents the technique for botnet detection using the distributed systems in the local area network. Distributed system contains host and network levels. At the host level, the botnets detection is based on Bayes classification. In order to perform the classification, the classes and subclasses were constructed on the basis of botnets patterns. An algorithm for classifier training was developed. The network level provides the exchange of the classification results for the knowledge transfer to the rest of the antivirus program units of the distributed system.