S. S. S. Reddy, Ramesh Shahabadkar, Ch Mamatha, P. Chatterjee
{"title":"使用机器学习和数据科学方法的网络位置保护策略","authors":"S. S. S. Reddy, Ramesh Shahabadkar, Ch Mamatha, P. Chatterjee","doi":"10.1109/ICATCCT.2017.8389140","DOIUrl":null,"url":null,"abstract":"A protection policy employs diverse techniques and mechanisms to sense protection related glitches and coercions in a networked location. The protection policy is big-data focused and services machine learning technique to perform protection analytics using data science. The protection policy performs entity behavioral analytics to sense the protection related glitches and coercions, regardless of whether such glitches/coercions were previously known. The protection policy can include both real-time and group modes for sensing glitches and coercions. By visually presenting diagnostic results scored with jeopardy assessments and auxiliary indication, the protection policy enables network protection administrators to respond to a sensed glitch or coercions, and to take action promptly.","PeriodicalId":123050,"journal":{"name":"2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Protection policy in networked locations using machine learning and data science approach\",\"authors\":\"S. S. S. Reddy, Ramesh Shahabadkar, Ch Mamatha, P. Chatterjee\",\"doi\":\"10.1109/ICATCCT.2017.8389140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A protection policy employs diverse techniques and mechanisms to sense protection related glitches and coercions in a networked location. The protection policy is big-data focused and services machine learning technique to perform protection analytics using data science. The protection policy performs entity behavioral analytics to sense the protection related glitches and coercions, regardless of whether such glitches/coercions were previously known. The protection policy can include both real-time and group modes for sensing glitches and coercions. By visually presenting diagnostic results scored with jeopardy assessments and auxiliary indication, the protection policy enables network protection administrators to respond to a sensed glitch or coercions, and to take action promptly.\",\"PeriodicalId\":123050,\"journal\":{\"name\":\"2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)\",\"volume\":\"159 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICATCCT.2017.8389140\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICATCCT.2017.8389140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Protection policy in networked locations using machine learning and data science approach
A protection policy employs diverse techniques and mechanisms to sense protection related glitches and coercions in a networked location. The protection policy is big-data focused and services machine learning technique to perform protection analytics using data science. The protection policy performs entity behavioral analytics to sense the protection related glitches and coercions, regardless of whether such glitches/coercions were previously known. The protection policy can include both real-time and group modes for sensing glitches and coercions. By visually presenting diagnostic results scored with jeopardy assessments and auxiliary indication, the protection policy enables network protection administrators to respond to a sensed glitch or coercions, and to take action promptly.