{"title":"基于机器学习的网络攻击下车辆队列零碰撞概率实现","authors":"M. Mongelli, Marco Muselli, Enrico Ferrari","doi":"10.1109/ICSRS48664.2019.8987644","DOIUrl":null,"url":null,"abstract":"In view of system reliability, extraction of knowledge from models of artificial intelligence may be more important than their forecasting ability. The elaboration of rules found by intelligible analytics gives here insight into the problem of packet falsification in vehicle platooning.","PeriodicalId":430931,"journal":{"name":"2019 4th International Conference on System Reliability and Safety (ICSRS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Achieving Zero Collision Probability in Vehicle Platooning under Cyber Attacks via Machine Learning\",\"authors\":\"M. Mongelli, Marco Muselli, Enrico Ferrari\",\"doi\":\"10.1109/ICSRS48664.2019.8987644\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In view of system reliability, extraction of knowledge from models of artificial intelligence may be more important than their forecasting ability. The elaboration of rules found by intelligible analytics gives here insight into the problem of packet falsification in vehicle platooning.\",\"PeriodicalId\":430931,\"journal\":{\"name\":\"2019 4th International Conference on System Reliability and Safety (ICSRS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 4th International Conference on System Reliability and Safety (ICSRS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSRS48664.2019.8987644\",\"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 4th International Conference on System Reliability and Safety (ICSRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSRS48664.2019.8987644","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Achieving Zero Collision Probability in Vehicle Platooning under Cyber Attacks via Machine Learning
In view of system reliability, extraction of knowledge from models of artificial intelligence may be more important than their forecasting ability. The elaboration of rules found by intelligible analytics gives here insight into the problem of packet falsification in vehicle platooning.