The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
스마트 홈 환경에서는 거주자의 활동이 포착될 때마다 센서가 이벤트를 생성합니다. 그러나 일부 요인으로 인해 기술적으로는 합리적이지만 실제 활동과 모순되는 비정상적인 이벤트가 발생할 수 있습니다. 비정상적인 이벤트를 탐지하기 위해 클러스터링 기반 또는 스냅샷 기반 접근 방식과 같은 다양한 방법이 도입되었습니다. 그러나 다수의 이벤트에서 발생하고 정상적인 센서 판독값 내에 혼합되는 복잡한 이상 현상을 처리하기에는 한계가 있습니다. 본 논문에서는 센서 간의 공간적 상관성과 신뢰성 있는 상관성을 고려하여 스마트 홈 환경에서 센서 이상을 탐지하는 새로운 방법을 제안한다. 처음에는 두 센서의 모든 쌍에 대한 상관관계를 미리 계산하여 관계를 알아냅니다. 그런 다음 주기적인 센서 판독값을 통해 사전 계산된 것과 일치하지 않는 관계가 있으면 상관된 센서에서 이상이 감지됩니다. 실제 데이터 세트를 사용한 광범위한 평가를 통해 제안된 방법이 탐지율이 20% 향상되고 합리적으로 낮은 오탐률로 이전 접근 방식보다 성능이 우수하다는 것을 보여줍니다.
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
스마트 홈, 센서, 이상, 공간 상관, 신뢰할 수 있는 상관관계
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부
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
부
@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},}
부
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 -