{"title":"一种分层的基于豆子的异常检测模型","authors":"B. Tian, K. Merrick, Shui Yu, Jiankun Hu","doi":"10.1109/ICCNC.2013.6504158","DOIUrl":null,"url":null,"abstract":"A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.","PeriodicalId":229123,"journal":{"name":"2013 International Conference on Computing, Networking and Communications (ICNC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A hierarchical pea-based anomaly detection model\",\"authors\":\"B. Tian, K. Merrick, Shui Yu, Jiankun Hu\",\"doi\":\"10.1109/ICCNC.2013.6504158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.\",\"PeriodicalId\":229123,\"journal\":{\"name\":\"2013 International Conference on Computing, Networking and Communications (ICNC)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-01-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Computing, Networking and Communications (ICNC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCNC.2013.6504158\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Computing, Networking and Communications (ICNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCNC.2013.6504158","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.