{"title":"HiNFRA:层次神经模糊学习在线风险评估","authors":"K. Haslum, A. Abraham, S. J. Knapskog","doi":"10.1109/AMS.2008.120","DOIUrl":null,"url":null,"abstract":"Our previous research illustrated the design of fuzzy logic based online risk assessment for Distributed Intrusion Prediction and Prevention Systems (DIPPS) [3]. Based on the DIPPS sensors, instead of merely preventing the attackers or blocking traffic, we propose a fuzzy logic based online risk assessment scheme. This paper propose a Hierarchical Neuro-Fuzzy online Risk Assessment (HiNFRA) model to aid the decision making process of a DIPPS. The fine tuning of fuzzy logic based risk assessment model is achieved using a neural network learning technique. Preliminary results indicate that the neural learning technique could improve the fuzzy controller performance and make the risk assessment model more robust.","PeriodicalId":122964,"journal":{"name":"2008 Second Asia International Conference on Modelling & Simulation (AMS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"HiNFRA: Hierarchical Neuro-Fuzzy Learning for Online Risk Assessment\",\"authors\":\"K. Haslum, A. Abraham, S. J. Knapskog\",\"doi\":\"10.1109/AMS.2008.120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Our previous research illustrated the design of fuzzy logic based online risk assessment for Distributed Intrusion Prediction and Prevention Systems (DIPPS) [3]. Based on the DIPPS sensors, instead of merely preventing the attackers or blocking traffic, we propose a fuzzy logic based online risk assessment scheme. This paper propose a Hierarchical Neuro-Fuzzy online Risk Assessment (HiNFRA) model to aid the decision making process of a DIPPS. The fine tuning of fuzzy logic based risk assessment model is achieved using a neural network learning technique. Preliminary results indicate that the neural learning technique could improve the fuzzy controller performance and make the risk assessment model more robust.\",\"PeriodicalId\":122964,\"journal\":{\"name\":\"2008 Second Asia International Conference on Modelling & Simulation (AMS)\",\"volume\":\"76 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Second Asia International Conference on Modelling & Simulation (AMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AMS.2008.120\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Second Asia International Conference on Modelling & Simulation (AMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AMS.2008.120","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
HiNFRA: Hierarchical Neuro-Fuzzy Learning for Online Risk Assessment
Our previous research illustrated the design of fuzzy logic based online risk assessment for Distributed Intrusion Prediction and Prevention Systems (DIPPS) [3]. Based on the DIPPS sensors, instead of merely preventing the attackers or blocking traffic, we propose a fuzzy logic based online risk assessment scheme. This paper propose a Hierarchical Neuro-Fuzzy online Risk Assessment (HiNFRA) model to aid the decision making process of a DIPPS. The fine tuning of fuzzy logic based risk assessment model is achieved using a neural network learning technique. Preliminary results indicate that the neural learning technique could improve the fuzzy controller performance and make the risk assessment model more robust.