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

IF 2.8 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Justin Baudisch, Birte Richter, Thorsten Jungeblut
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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.

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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。
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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).
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