Pietro Ferrara, A. K. Mandal, Agostino Cortesi, F. Spoto
{"title":"Cross-Programming Language Taint Analysis for the IoT Ecosystem","authors":"Pietro Ferrara, A. K. Mandal, Agostino Cortesi, F. Spoto","doi":"10.14279/tuj.eceasst.77.1104","DOIUrl":null,"url":null,"abstract":"The Internet of Things (IoT) is a key component for the next disruptive technologies. However, IoT merges together several diverse software layers: embedded, enterprise, and cloud programs interact with each other. In addition, security and privacy vulnerabilities of IoT software might be particularly dangerous due to the pervasiveness and physical nature of these systems. During the last decades, static analysis, and in particular taint analysis, has been widely applied to detect software vulnerabilities. Unfortunately, these analyses assume that software is entirely written in a single programming language, and they are not immediately suitable to detect IoT vulnerabilities where many different software components, written in different programming languages, interact. This paper discusses how to leverage existing static taint analyses to a cross-programming language scenario.","PeriodicalId":115235,"journal":{"name":"Electron. Commun. Eur. Assoc. Softw. Sci. Technol.","volume":"159 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electron. Commun. Eur. Assoc. Softw. Sci. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14279/tuj.eceasst.77.1104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The Internet of Things (IoT) is a key component for the next disruptive technologies. However, IoT merges together several diverse software layers: embedded, enterprise, and cloud programs interact with each other. In addition, security and privacy vulnerabilities of IoT software might be particularly dangerous due to the pervasiveness and physical nature of these systems. During the last decades, static analysis, and in particular taint analysis, has been widely applied to detect software vulnerabilities. Unfortunately, these analyses assume that software is entirely written in a single programming language, and they are not immediately suitable to detect IoT vulnerabilities where many different software components, written in different programming languages, interact. This paper discusses how to leverage existing static taint analyses to a cross-programming language scenario.