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Authors: Martin Jänicke 1 ; Viktor Schmidt 1 ; Bernhard Sick 1 ; Sven Tomforde 1 and Paul Lukowicz 2

Affiliations: 1 University of Kassel, Germany ; 2 German Research Center for Artificial Intelligence, Germany

ISBN: 978-989-758-275-2

Keyword(s): Smart Devices, Gaussian Mixture Model, Organic Computing, Self-Awareness, Probabilistic theft detection

Related Ontology Subjects/Areas/Topics: Ambient Intelligence ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Intelligent User Interfaces ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Model-Based Reasoning ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: Personal devices such as smart phones are increasingly utilised in everyday life. Frequently, Activity Recognition is performed on these devices to estimate the current user status and trigger automated actions according to the user's needs. In this article, we focus on improving the self-awareness of such systems in terms of detecting theft: We equip devices with the capabilities to model their own user and to, e.g., alarm the legal user if an unexpected other person is carrying the device. We gathered 24hours of data in a case study with 14 persons using a Nokia N97 and trained an activity recognition system. Based on it, we developed and investigated an autonomous novelty detection system that continuously checks if the observed user behavior corresponds to the initial model, and that gives an alarm if not. Our evaluations show that the presented method is highly successful with a successful theft detection rate of over 85\% for the trained set of persons. Comparison experim ents with state of the art techniques support the strong practicality of our approach. (More)

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Paper citation in several formats:
Jänicke, M.; Schmidt, V.; Sick, B.; Tomforde, S. and Lukowicz, P. (2018). Hijacked Smart Devices - Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 131-142. DOI: 10.5220/0006594901310142

@conference{icaart18,
author={Martin Jänicke. and Viktor Schmidt. and Bernhard Sick. and Sven Tomforde. and Paul Lukowicz.},
title={Hijacked Smart Devices - Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={131-142},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006594901310142},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Hijacked Smart Devices - Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection
SN - 978-989-758-275-2
AU - Jänicke, M.
AU - Schmidt, V.
AU - Sick, B.
AU - Tomforde, S.
AU - Lukowicz, P.
PY - 2018
SP - 131
EP - 142
DO - 10.5220/0006594901310142

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