{"title":"神经网络在计算机网络安全评估中的应用研究","authors":"Zhou Lianbing","doi":"10.1109/ICMTMA.2016.157","DOIUrl":null,"url":null,"abstract":"In this paper, we aim to solve the problem of neural network in computer network security assessment, which is very important for computer network's popularization. Our proposed computer network security assessment system contains client and server. The client module includes: 1) scanning configuration model, 2) assessment model, 3) scanning result database model and 4) output model. Furthermore, server is made up of 1) scanning engine, 2) vulnerability database, 3) rules database. To promote the performance of artificial neural network, we choose the back propagation neural network, and particle swarm optimization is utilized to optimize parameters. Finally, experimental results demonstrate the effectiveness of our proposed approach.","PeriodicalId":318523,"journal":{"name":"2016 Eighth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Study on Applying the Neural Network in Computer Network Security Assessment\",\"authors\":\"Zhou Lianbing\",\"doi\":\"10.1109/ICMTMA.2016.157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we aim to solve the problem of neural network in computer network security assessment, which is very important for computer network's popularization. Our proposed computer network security assessment system contains client and server. The client module includes: 1) scanning configuration model, 2) assessment model, 3) scanning result database model and 4) output model. Furthermore, server is made up of 1) scanning engine, 2) vulnerability database, 3) rules database. To promote the performance of artificial neural network, we choose the back propagation neural network, and particle swarm optimization is utilized to optimize parameters. Finally, experimental results demonstrate the effectiveness of our proposed approach.\",\"PeriodicalId\":318523,\"journal\":{\"name\":\"2016 Eighth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Eighth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMTMA.2016.157\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Eighth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMTMA.2016.157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on Applying the Neural Network in Computer Network Security Assessment
In this paper, we aim to solve the problem of neural network in computer network security assessment, which is very important for computer network's popularization. Our proposed computer network security assessment system contains client and server. The client module includes: 1) scanning configuration model, 2) assessment model, 3) scanning result database model and 4) output model. Furthermore, server is made up of 1) scanning engine, 2) vulnerability database, 3) rules database. To promote the performance of artificial neural network, we choose the back propagation neural network, and particle swarm optimization is utilized to optimize parameters. Finally, experimental results demonstrate the effectiveness of our proposed approach.