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A Data-Centric Anomaly-Based Detection System for Interactive Machine Learning Setups
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0003-0546-072X
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-9471-8405
2022 (English)In: Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST, SciTePress, 2022, p. 182-189Conference paper, Published paper (Refereed)
Abstract [en]

A major concern in the use of Internet of Things (IoT) technologies in general is their reliability in the presence of security threats and cyberattacks. Particularly, there is a growing recognition that IoT environments featuring virtual sensing and interactive machine learning may be subject to additional vulnerabilities when compared to traditional networks and classical batch learning settings. Partly, this is as adversaries could more easily manipulate the user feedback channel with malicious content. To this end, we propose a data-centric anomaly-based detection system, based on machine learning, that facilitates the process of identifying anomalies, particularly those related to poisoning integrity attacks targeting the user feedback channel of interactive machine learning setups. We demonstrate the capabilities of the proposed system in a case study involving a smart campus setup consisting of different smart devices, namely, a smart camera, a climate sensmitter, smart lighting, a smart phone, and a user feedback channel over which users could furnish labels to improve detection of correct system states, namely, activity types happening inside a room. Our results indicate that anomalies targeting the user feedback channel can be accurately detected at 98% using the Random Forest classifier.

Place, publisher, year, edition, pages
SciTePress, 2022. p. 182-189
Series
WEBIST, E-ISSN 2184-3252
Keywords [en]
Anomaly Detection, Interactive Machine Learning, Internet of Things, Virtual Sensors, Intrusion Detection, Poisoning Attack, IoT Security
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mau:diva-55923DOI: 10.5220/0011560100003318Scopus ID: 2-s2.0-85146200321ISBN: 978-989-758-613-2 (electronic)OAI: oai:DiVA.org:mau-55923DiVA, id: diva2:1709985
Conference
18th International Conference on Web Information Systems and Technologies - WEBIST, 2022 , Valletta, Malta
Available from: 2022-11-10 Created: 2022-11-10 Last updated: 2023-12-12Bibliographically approved

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Bugeja, JosephPersson, Jan A.

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Citation style
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