A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment.

IF 2.8 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Kunstliche Intelligenz Pub Date : 2022-01-01 DOI:10.1007/s13218-022-00775-5
Justin Baudisch, Birte Richter, Thorsten Jungeblut
{"title":"A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment.","authors":"Justin Baudisch,&nbsp;Birte Richter,&nbsp;Thorsten Jungeblut","doi":"10.1007/s13218-022-00775-5","DOIUrl":null,"url":null,"abstract":"<p><p>This paper presents a framework for learning event sequences for anomaly detection in a smart home environment. It addresses environment conditions, device grouping, system performance and explainability of anomalies. Our method models user behavior as sequences of events, triggered by interaction of the home residents with the Internet of Things (IoT) devices. Based on a given set of recorded event sequences, the system can learn the habitual behavior of the residents. An anomaly is described as deviation from that normal behavior, previously learned by the system. One key feature of our framework is the explainability of detected anomalies, which is implemented through a simple rule analysis.</p>","PeriodicalId":45413,"journal":{"name":"Kunstliche Intelligenz","volume":"36 3-4","pages":"259-266"},"PeriodicalIF":2.8000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9619011/pdf/","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kunstliche Intelligenz","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13218-022-00775-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 1

Abstract

This paper presents a framework for learning event sequences for anomaly detection in a smart home environment. It addresses environment conditions, device grouping, system performance and explainability of anomalies. Our method models user behavior as sequences of events, triggered by interaction of the home residents with the Internet of Things (IoT) devices. Based on a given set of recorded event sequences, the system can learn the habitual behavior of the residents. An anomaly is described as deviation from that normal behavior, previously learned by the system. One key feature of our framework is the explainability of detected anomalies, which is implemented through a simple rule analysis.

Abstract Image

Abstract Image

Abstract Image

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
学习事件序列和解释智能家居环境中检测到的异常的框架。
本文提出了一个学习事件序列的框架,用于智能家居环境中的异常检测。它涉及环境条件、设备分组、系统性能和异常的可解释性。我们的方法将用户行为建模为一系列事件,由家庭居民与物联网(IoT)设备的交互触发。基于一组给定的记录事件序列,系统可以学习居民的习惯行为。异常被描述为系统先前学习到的对正常行为的偏离。我们框架的一个关键特征是检测到的异常的可解释性,这是通过简单的规则分析实现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Kunstliche Intelligenz
Kunstliche Intelligenz COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
8.60
自引率
3.40%
发文量
32
期刊介绍: Artificial Intelligence has successfully established itself as a scientific discipline in research and education and has become an integral part of Computer Science with an interdisciplinary character. AI deals with both the development of information processing systems that deliver “intelligent” services and with the modeling of human cognitive skills with the help of information processing systems. Research, development and applications in the field of AI pursue the general goal of creating processes for taking in and processing information that more closely resemble human problem-solving behavior, and to subsequently use those processes to derive methods that enhance and qualitatively improve conventional information processing systems. KI – Künstliche Intelligenz is the official journal of the division for artificial intelligence within the ''Gesellschaft für Informatik e.V.'' (GI) – the German Informatics Society – with contributions from the entire field of artificial intelligence. The journal presents fundamentals and tools, their use and adaptation for scientific purposes, and applications that are implemented using AI methods – and thus provides readers with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. A highly reputed team of editors from both university and industry will ensure the scientific quality of the articles.The journal provides all members of the AI community with quick access to current topics in the field, while also promoting vital interdisciplinary interchange, it will as well serve as a media of communication between the members of the division and the parent society. The journal is published in English. Content published in this journal is peer reviewed (Double Blind).
期刊最新文献
In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies. Some Thoughts on AI Stimulated by Michael Wooldridge's Book "The Road to Conscious Machines. The Story of AI". A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment. Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs. News.
×
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