An Anomaly Detection Study for the Smart Home Environment

Mehmet Erhan Bilgin; H. Hakan Kilinc; Abdul Halim Zaim

doi:10.1109/UBMK55850.2022.9919448

An Anomaly Detection Study for the Smart Home Environment

Mehmet Erhan Bilgin
, H. Hakan Kilinc
, Abdul Halim Zaim

Istanbul Ticaret University
Orion Innovation Turkey

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

7 Atıf (Scopus)

Özet

Unusual sensor data in smart homes may herald different problems based on sensor errors, security vulnera-bilities, activity and behavior changes. This study focuses on detecting anomalies and unusual situations in 7 different sensor data in a house. For this, a model created with a combination of unsupervised and supervised machine learning algorithms is used. The sensor data are labeled using Isolation Forest which is one of the unsupervised algorithms. Then, the data is trained with the supervised algorithms Decision Tree, Extra Trees, Random Forest and XGBoost classification algorithms. Anomaly decisions are made with an accuracy of over 99 percent.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	31-36
Sayfa sayısı	6
ISBN (Elektronik)	9781665470100
DOI'lar	https://doi.org/10.1109/UBMK55850.2022.9919448
Yayın durumu	Yayınlandı - 2022
Harici olarak yayınlandı	Evet
Etkinlik	7th International Conference on Computer Science and Engineering, UBMK 2022 - Diyarbakir, Türkiye Süre: 14 Eyl 2022 → 16 Eyl 2022

Yayın serisi

Adı	Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	7th International Conference on Computer Science and Engineering, UBMK 2022
Ülke/Bölge	Türkiye
Şehir	Diyarbakir
Periyot	14/09/22 → 16/09/22

Bibliyografik not

Publisher Copyright:
© 2022 IEEE.

Belgeye Erişim

10.1109/UBMK55850.2022.9919448

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Bilgin, M. E., Kilinc, H. H., & Zaim, A. H. (2022). An Anomaly Detection Study for the Smart Home Environment. In Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022 (pp. 31-36). (Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UBMK55850.2022.9919448

Bilgin, Mehmet Erhan ; Kilinc, H. Hakan ; Zaim, Abdul Halim. / An Anomaly Detection Study for the Smart Home Environment. Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 31-36 (Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022).

@inproceedings{6e8df06d4ce64509b67a43dcde0e6d39,

title = "An Anomaly Detection Study for the Smart Home Environment",

abstract = "Unusual sensor data in smart homes may herald different problems based on sensor errors, security vulnera-bilities, activity and behavior changes. This study focuses on detecting anomalies and unusual situations in 7 different sensor data in a house. For this, a model created with a combination of unsupervised and supervised machine learning algorithms is used. The sensor data are labeled using Isolation Forest which is one of the unsupervised algorithms. Then, the data is trained with the supervised algorithms Decision Tree, Extra Trees, Random Forest and XGBoost classification algorithms. Anomaly decisions are made with an accuracy of over 99 percent.",

keywords = "Anomaly Detection, Edge Computing, IoT, Machine Learning, Smart Home",

author = "Bilgin, \{Mehmet Erhan\} and Kilinc, \{H. Hakan\} and Zaim, \{Abdul Halim\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 7th International Conference on Computer Science and Engineering, UBMK 2022 ; Conference date: 14-09-2022 Through 16-09-2022",

year = "2022",

doi = "10.1109/UBMK55850.2022.9919448",

language = "English",

series = "Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "31--36",

booktitle = "Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022",

address = "United States",

}

Bilgin, ME, Kilinc, HH & Zaim, AH 2022, An Anomaly Detection Study for the Smart Home Environment. in Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022, Institute of Electrical and Electronics Engineers Inc., pp. 31-36, 7th International Conference on Computer Science and Engineering, UBMK 2022, Diyarbakir, Türkiye, 14/09/22. https://doi.org/10.1109/UBMK55850.2022.9919448

An Anomaly Detection Study for the Smart Home Environment. / Bilgin, Mehmet Erhan; Kilinc, H. Hakan; Zaim, Abdul Halim.
Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 31-36 (Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - An Anomaly Detection Study for the Smart Home Environment

AU - Bilgin, Mehmet Erhan

AU - Kilinc, H. Hakan

AU - Zaim, Abdul Halim

PY - 2022

Y1 - 2022

N2 - Unusual sensor data in smart homes may herald different problems based on sensor errors, security vulnera-bilities, activity and behavior changes. This study focuses on detecting anomalies and unusual situations in 7 different sensor data in a house. For this, a model created with a combination of unsupervised and supervised machine learning algorithms is used. The sensor data are labeled using Isolation Forest which is one of the unsupervised algorithms. Then, the data is trained with the supervised algorithms Decision Tree, Extra Trees, Random Forest and XGBoost classification algorithms. Anomaly decisions are made with an accuracy of over 99 percent.

AB - Unusual sensor data in smart homes may herald different problems based on sensor errors, security vulnera-bilities, activity and behavior changes. This study focuses on detecting anomalies and unusual situations in 7 different sensor data in a house. For this, a model created with a combination of unsupervised and supervised machine learning algorithms is used. The sensor data are labeled using Isolation Forest which is one of the unsupervised algorithms. Then, the data is trained with the supervised algorithms Decision Tree, Extra Trees, Random Forest and XGBoost classification algorithms. Anomaly decisions are made with an accuracy of over 99 percent.

KW - Anomaly Detection

KW - Edge Computing

KW - IoT

KW - Machine Learning

KW - Smart Home

UR - https://www.scopus.com/pages/publications/85141836847

U2 - 10.1109/UBMK55850.2022.9919448

DO - 10.1109/UBMK55850.2022.9919448

M3 - Conference contribution

AN - SCOPUS:85141836847

T3 - Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022

SP - 31

EP - 36

BT - Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th International Conference on Computer Science and Engineering, UBMK 2022

Y2 - 14 September 2022 through 16 September 2022

ER -

Bilgin ME, Kilinc HH, Zaim AH. An Anomaly Detection Study for the Smart Home Environment. In Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 31-36. (Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022). doi: 10.1109/UBMK55850.2022.9919448

An Anomaly Detection Study for the Smart Home Environment

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