CAN协议的自动逆向工程

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Network World Pub Date : 2021-01-01 DOI:10.14311/nnw.2021.31.015

Nils Weiss, Enrico Pozzobon, J. Mottok, V. Matousek

{"title":"CAN协议的自动逆向工程","authors":"Nils Weiss, Enrico Pozzobon, J. Mottok, V. Matousek","doi":"10.14311/nnw.2021.31.015","DOIUrl":null,"url":null,"abstract":"Car manufacturers define proprietary protocols to be used inside their vehicular networks, which are kept an industrial secret, therefore impeding independent researchers from extracting information from these networks. This article describes a statistical and a neural network approach that allows reverse engineering proprietary controller area network (CAN)-protocols assuming they were designed using the data base CAN (DBC) file format. The proposed algorithms are tested with CAN traces taken from a real car. We show that our approaches can correctly reverse engineer CAN messages in an automated manner.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated Reverse Engineering of CAN Protocols\",\"authors\":\"Nils Weiss, Enrico Pozzobon, J. Mottok, V. Matousek\",\"doi\":\"10.14311/nnw.2021.31.015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Car manufacturers define proprietary protocols to be used inside their vehicular networks, which are kept an industrial secret, therefore impeding independent researchers from extracting information from these networks. This article describes a statistical and a neural network approach that allows reverse engineering proprietary controller area network (CAN)-protocols assuming they were designed using the data base CAN (DBC) file format. The proposed algorithms are tested with CAN traces taken from a real car. We show that our approaches can correctly reverse engineer CAN messages in an automated manner.\",\"PeriodicalId\":49765,\"journal\":{\"name\":\"Neural Network World\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Network World\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.14311/nnw.2021.31.015\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Network World","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.14311/nnw.2021.31.015","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

摘要

汽车制造商为其车载网络定义了专有协议，这些协议是行业机密，因此阻碍了独立研究人员从这些网络中提取信息。本文描述了一种统计和神经网络方法，该方法允许对专有控制器局域网(CAN)协议进行逆向工程，假设它们是使用数据库CAN (DBC)文件格式设计的。用一辆真实汽车的CAN轨迹对所提出的算法进行了测试。我们表明，我们的方法可以正确地以自动化的方式逆向工程can消息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Automated Reverse Engineering of CAN Protocols

Car manufacturers define proprietary protocols to be used inside their vehicular networks, which are kept an industrial secret, therefore impeding independent researchers from extracting information from these networks. This article describes a statistical and a neural network approach that allows reverse engineering proprietary controller area network (CAN)-protocols assuming they were designed using the data base CAN (DBC) file format. The proposed algorithms are tested with CAN traces taken from a real car. We show that our approaches can correctly reverse engineer CAN messages in an automated manner.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Neural Network World 工程技术-计算机：人工智能

CiteScore

1.80

自引率

0.00%

发文量

审稿时长

12 months

期刊介绍： Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.