{"title":"A Knowledge Graph Question Answering Approach to IoT Forensics","authors":"Ruipeng Zhang, Mengjun Xie","doi":"10.1145/3576842.3589161","DOIUrl":null,"url":null,"abstract":"Internet of Things (IoT) forensics has been a particularly challenging task for forensic practitioners due to the heterogeneity of IoT environments as well as the complexity and volume of IoT data. With the advent of artificial intelligence, question-answering (QA) systems have emerged as a potential solution for users to access sophisticated forensic knowledge and data. In this light, we present a novel IoT forensics framework that employs knowledge graph question answering (KGQA). Our framework enables investigators to access forensic artifacts and cybersecurity knowledge using natural language questions facilitated by a deep-learning-powered KGQA model. The proposed framework demonstrates high efficacy in answering natural language questions over the experimental IoT forensic knowledge graph.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3576842.3589161","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Internet of Things (IoT) forensics has been a particularly challenging task for forensic practitioners due to the heterogeneity of IoT environments as well as the complexity and volume of IoT data. With the advent of artificial intelligence, question-answering (QA) systems have emerged as a potential solution for users to access sophisticated forensic knowledge and data. In this light, we present a novel IoT forensics framework that employs knowledge graph question answering (KGQA). Our framework enables investigators to access forensic artifacts and cybersecurity knowledge using natural language questions facilitated by a deep-learning-powered KGQA model. The proposed framework demonstrates high efficacy in answering natural language questions over the experimental IoT forensic knowledge graph.