IEICE TRANSACTIONS on Information
Effective Anomaly Detection in Smart Home by Analyzing Sensor Correlations
Summary :
In a smart home environment, sensors generate events whenever activities of residents are captured. However, due to some factors, abnormal events could be generated, which are technically reasonable but contradict to real-world activities. To detect abnormal events, a number of methods has been introduced, e.g., clustering-based or snapshot-based approaches. However, they have limitations to deal with complicated anomalies which occur with large number of events and blended within normal sensor readings. In this paper, we propose a novel method of detecting sensor anomalies under smart home environment by considering spatial correlation and dependable correlation between sensors. Initially, we pre-calculate these correlations of every pair of two sensors to discover their relations. Then, from periodic sensor readings, if it has any unmatched relations to the pre-computed ones, an anomaly is detected on the correlated sensor. Through extensive evaluations with real datasets, we show that the proposed method outperforms previous approaches with 20% improvement on detection rate and reasonably low false positive rate.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.2 pp.332-336
- Publication Date
- 2021/02/01
- Publicized
- 2020/11/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDL8056
- Type of Manuscript
- LETTER
- Category
- Dependable Computing
Authors
Giang-Truong NGUYEN
Chonnam National University
Van-Quyet NGUYEN
Hung Yen University of Technology and Education
Van-Hau NGUYEN
Hung Yen University of Technology and Education
Kyungbaek KIM
Chonnam National University
Keyword
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Giang-Truong NGUYEN, Van-Quyet NGUYEN, Van-Hau NGUYEN, Kyungbaek KIM, "Effective Anomaly Detection in Smart Home by Analyzing Sensor Correlations" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 2, pp. 332-336, February 2021, doi: 10.1587/transinf.2020EDL8056.
Abstract: In a smart home environment, sensors generate events whenever activities of residents are captured. However, due to some factors, abnormal events could be generated, which are technically reasonable but contradict to real-world activities. To detect abnormal events, a number of methods has been introduced, e.g., clustering-based or snapshot-based approaches. However, they have limitations to deal with complicated anomalies which occur with large number of events and blended within normal sensor readings. In this paper, we propose a novel method of detecting sensor anomalies under smart home environment by considering spatial correlation and dependable correlation between sensors. Initially, we pre-calculate these correlations of every pair of two sensors to discover their relations. Then, from periodic sensor readings, if it has any unmatched relations to the pre-computed ones, an anomaly is detected on the correlated sensor. Through extensive evaluations with real datasets, we show that the proposed method outperforms previous approaches with 20% improvement on detection rate and reasonably low false positive rate.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8056/_p
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@ARTICLE{e104-d_2_332,
author={Giang-Truong NGUYEN, Van-Quyet NGUYEN, Van-Hau NGUYEN, Kyungbaek KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Effective Anomaly Detection in Smart Home by Analyzing Sensor Correlations},
year={2021},
volume={E104-D},
number={2},
pages={332-336},
abstract={In a smart home environment, sensors generate events whenever activities of residents are captured. However, due to some factors, abnormal events could be generated, which are technically reasonable but contradict to real-world activities. To detect abnormal events, a number of methods has been introduced, e.g., clustering-based or snapshot-based approaches. However, they have limitations to deal with complicated anomalies which occur with large number of events and blended within normal sensor readings. In this paper, we propose a novel method of detecting sensor anomalies under smart home environment by considering spatial correlation and dependable correlation between sensors. Initially, we pre-calculate these correlations of every pair of two sensors to discover their relations. Then, from periodic sensor readings, if it has any unmatched relations to the pre-computed ones, an anomaly is detected on the correlated sensor. Through extensive evaluations with real datasets, we show that the proposed method outperforms previous approaches with 20% improvement on detection rate and reasonably low false positive rate.},
keywords={},
doi={10.1587/transinf.2020EDL8056},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Effective Anomaly Detection in Smart Home by Analyzing Sensor Correlations
T2 - IEICE TRANSACTIONS on Information
SP - 332
EP - 336
AU - Giang-Truong NGUYEN
AU - Van-Quyet NGUYEN
AU - Van-Hau NGUYEN
AU - Kyungbaek KIM
PY - 2021
DO - 10.1587/transinf.2020EDL8056
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2021
AB - In a smart home environment, sensors generate events whenever activities of residents are captured. However, due to some factors, abnormal events could be generated, which are technically reasonable but contradict to real-world activities. To detect abnormal events, a number of methods has been introduced, e.g., clustering-based or snapshot-based approaches. However, they have limitations to deal with complicated anomalies which occur with large number of events and blended within normal sensor readings. In this paper, we propose a novel method of detecting sensor anomalies under smart home environment by considering spatial correlation and dependable correlation between sensors. Initially, we pre-calculate these correlations of every pair of two sensors to discover their relations. Then, from periodic sensor readings, if it has any unmatched relations to the pre-computed ones, an anomaly is detected on the correlated sensor. Through extensive evaluations with real datasets, we show that the proposed method outperforms previous approaches with 20% improvement on detection rate and reasonably low false positive rate.
ER -
English
