过程系统中增强的安全性——评估知识援助

A. Lohfink, V. M. Memmesheimer, Frederike Gartzky, C. Garth
{"title":"过程系统中增强的安全性——评估知识援助","authors":"A. Lohfink, V. M. Memmesheimer, Frederike Gartzky, C. Garth","doi":"10.1109/TREX53765.2021.00006","DOIUrl":null,"url":null,"abstract":"We present evaluation results of our enhancements to the Security in Process System [14] developed by Lohfink et al. to support triage analysis in operational technology networks. To ensure fast and appropriate reactions to anomalies in device readings, this system communicates anomaly detection results and device readings to incorporate human expertise and experience. It exploits periodical behavior in the data combining spiral plots with results from anomaly detection. To support decisions, increase trust, and support cooperation in the system we enhanced it to be knowledge-assisted. A central knowledge base allows sharing knowledge between users and support during analysis. It consists of an ontology describing incidents, and a data base holding collections of exemplary sensor readings with annotations and visualization parameters. Related knowledge is proposed automatically and incorporated directly in the visualization to provide assistance that is closely coupled to the application, without additional hurdles. This integration is designed aiming on additional support for correct and fast detection of anomalies in the visualized device readings. We evaluate our enhancements to the Security in Process System in terms of effectiveness, efficiency, user satisfaction, and cognitive load with a detailed user study. Comparing the original and enhanced system, we are able to draw conclusions as to how our design narrows the knowledge gap between professional analysts and laymen. Furthermore, we present and discuss the results and impact on our future research.","PeriodicalId":345585,"journal":{"name":"2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Enhanced Security in Process System - Evaluating Knowledge Assistance\",\"authors\":\"A. Lohfink, V. M. Memmesheimer, Frederike Gartzky, C. Garth\",\"doi\":\"10.1109/TREX53765.2021.00006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present evaluation results of our enhancements to the Security in Process System [14] developed by Lohfink et al. to support triage analysis in operational technology networks. To ensure fast and appropriate reactions to anomalies in device readings, this system communicates anomaly detection results and device readings to incorporate human expertise and experience. It exploits periodical behavior in the data combining spiral plots with results from anomaly detection. To support decisions, increase trust, and support cooperation in the system we enhanced it to be knowledge-assisted. A central knowledge base allows sharing knowledge between users and support during analysis. It consists of an ontology describing incidents, and a data base holding collections of exemplary sensor readings with annotations and visualization parameters. Related knowledge is proposed automatically and incorporated directly in the visualization to provide assistance that is closely coupled to the application, without additional hurdles. This integration is designed aiming on additional support for correct and fast detection of anomalies in the visualized device readings. We evaluate our enhancements to the Security in Process System in terms of effectiveness, efficiency, user satisfaction, and cognitive load with a detailed user study. Comparing the original and enhanced system, we are able to draw conclusions as to how our design narrows the knowledge gap between professional analysts and laymen. Furthermore, we present and discuss the results and impact on our future research.\",\"PeriodicalId\":345585,\"journal\":{\"name\":\"2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TREX53765.2021.00006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TREX53765.2021.00006","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

我们展示了对Lohfink等人开发的过程安全系统[14]的改进的评估结果,以支持运营技术网络中的分类分析。为了确保对设备读数中的异常做出快速和适当的反应,该系统将异常检测结果和设备读数进行通信,以结合人类的专业知识和经验。它将螺旋图与异常检测结果相结合,利用数据的周期性行为。为了支持决策、增加信任和支持系统中的合作,我们将其增强为知识辅助。中心知识库允许在分析期间在用户和支持人员之间共享知识。它由描述事件的本体和包含示例传感器读数集合的数据库组成,其中包含注释和可视化参数。相关知识被自动提出,并直接合并到可视化中,以提供与应用程序紧密耦合的帮助,而没有额外的障碍。这种集成旨在为可视化设备读数中正确和快速检测异常提供额外支持。我们通过详细的用户研究,从有效性、效率、用户满意度和认知负荷等方面评估了我们对过程安全系统的改进。对比原来的系统和增强后的系统,我们可以得出结论,我们的设计是如何缩小专业分析师和外行之间的知识差距的。此外,我们提出并讨论了结果和对我们未来研究的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The Enhanced Security in Process System - Evaluating Knowledge Assistance
We present evaluation results of our enhancements to the Security in Process System [14] developed by Lohfink et al. to support triage analysis in operational technology networks. To ensure fast and appropriate reactions to anomalies in device readings, this system communicates anomaly detection results and device readings to incorporate human expertise and experience. It exploits periodical behavior in the data combining spiral plots with results from anomaly detection. To support decisions, increase trust, and support cooperation in the system we enhanced it to be knowledge-assisted. A central knowledge base allows sharing knowledge between users and support during analysis. It consists of an ontology describing incidents, and a data base holding collections of exemplary sensor readings with annotations and visualization parameters. Related knowledge is proposed automatically and incorporated directly in the visualization to provide assistance that is closely coupled to the application, without additional hurdles. This integration is designed aiming on additional support for correct and fast detection of anomalies in the visualized device readings. We evaluate our enhancements to the Security in Process System in terms of effectiveness, efficiency, user satisfaction, and cognitive load with a detailed user study. Comparing the original and enhanced system, we are able to draw conclusions as to how our design narrows the knowledge gap between professional analysts and laymen. Furthermore, we present and discuss the results and impact on our future research.
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
期刊最新文献
Should I Follow this Model? The Effect of Uncertainty Visualization on the Acceptance of Time Series Forecasts Making and Trusting Decisions in Visual Analytics A Case Study of Using Analytic Provenance to Reconstruct User Trust in a Guided Visual Analytics System Evaluating Forecasting, Knowledge, and Visual Analytics How to deal with Uncertainty in Machine Learning for Medical Imaging?
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1