自主信息物理生产系统中与信任相关的风险——调查

Maryam Zahid, Alessio Bucaioni, Francesco Flammini
{"title":"自主信息物理生产系统中与信任相关的风险——调查","authors":"Maryam Zahid, Alessio Bucaioni, Francesco Flammini","doi":"10.1109/CSR57506.2023.10224955","DOIUrl":null,"url":null,"abstract":"The production industry is looking for new solutions to improve the reliability, safety and efficiency of traditional processes. Current developments in artificial intelligence and machine learning have enabled a high level of autonomy in smart-manufacturing and production systems within Industry 4.0, thus paving the way towards fully Autonomous Cyber-Physical Production Systems (ACPPS). Although ACPPS can have many advantages, there still remains a concern regarding how much we can trust those systems, due to limited predictability, transparency, and explainability, as well as emerging vulnerabilities related to machine learning systems. In this paper, we present the findings of a study conducted on the possible risks related to the trustworthiness of ACPPS, and the consequences they have on the system and its environment.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Trustworthiness-Related Risks in Autonomous Cyber-Physical Production Systems - A Survey\",\"authors\":\"Maryam Zahid, Alessio Bucaioni, Francesco Flammini\",\"doi\":\"10.1109/CSR57506.2023.10224955\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The production industry is looking for new solutions to improve the reliability, safety and efficiency of traditional processes. Current developments in artificial intelligence and machine learning have enabled a high level of autonomy in smart-manufacturing and production systems within Industry 4.0, thus paving the way towards fully Autonomous Cyber-Physical Production Systems (ACPPS). Although ACPPS can have many advantages, there still remains a concern regarding how much we can trust those systems, due to limited predictability, transparency, and explainability, as well as emerging vulnerabilities related to machine learning systems. In this paper, we present the findings of a study conducted on the possible risks related to the trustworthiness of ACPPS, and the consequences they have on the system and its environment.\",\"PeriodicalId\":354918,\"journal\":{\"name\":\"2023 IEEE International Conference on Cyber Security and Resilience (CSR)\",\"volume\":\"104 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE International Conference on Cyber Security and Resilience (CSR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSR57506.2023.10224955\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSR57506.2023.10224955","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

生产行业正在寻找新的解决方案,以提高传统工艺的可靠性、安全性和效率。目前人工智能和机器学习的发展使工业4.0中的智能制造和生产系统具有高度的自主性,从而为完全自主的网络物理生产系统(ACPPS)铺平了道路。尽管ACPPS有很多优势,但由于有限的可预测性、透明度和可解释性,以及与机器学习系统相关的新漏洞,我们对这些系统的信任程度仍然存在担忧。在本文中,我们提出了一项关于ACPPS可信度可能存在的风险的研究结果,以及它们对系统及其环境的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trustworthiness-Related Risks in Autonomous Cyber-Physical Production Systems - A Survey
The production industry is looking for new solutions to improve the reliability, safety and efficiency of traditional processes. Current developments in artificial intelligence and machine learning have enabled a high level of autonomy in smart-manufacturing and production systems within Industry 4.0, thus paving the way towards fully Autonomous Cyber-Physical Production Systems (ACPPS). Although ACPPS can have many advantages, there still remains a concern regarding how much we can trust those systems, due to limited predictability, transparency, and explainability, as well as emerging vulnerabilities related to machine learning systems. In this paper, we present the findings of a study conducted on the possible risks related to the trustworthiness of ACPPS, and the consequences they have on the system and its environment.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信