Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios

Almaguer-Angeles, Fernando; Murphy, John; Murphy, Liam; Portillo-Dominguez, A. Omar

doi:10.1109/wf-iot.2019.8767357

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…20 keywords and ranks 1 of all keywords. Internet of things 12 [12], [21], [27], [34], [39] [52]- [55], [57], [59], [62], 02 Machine learning 11 [12], [37], [44], [45], [48], [54], [57], [62], [64], [70], [80]…”

Section: Trends In Smart Building Researchmentioning

confidence: 99%

Smart building trend, role, and position: a systematic literature review

Saputra

Ramadhan²

2023

IJRES

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<p>Property growth, especially in high-rise buildings in developed and developing countries, has experienced a significant increase. The increasingly expensive resources and the development of information technology encourage the growth of smart buildings in almost all countries, especially in developed and developing countries, and become a trend. The Internet of things (IoT) is the main driver in the development of smart buildings. Property businesses are competing to adopt and implement smart buildings on their properties. This is further strengthened by the development of smart cities in almost all cities around the world, one of the criteria is a smart community. The purpose of implementing smart buildings is to make property management effective and efficient. Using the systematic literature review, this paper will discuss what components must be met for a property called a smart building, then what is the role of smart buildings for an area or community, and how the current smart building trend will be in the future.</p>

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Section: Trends In Smart Building Researchmentioning

confidence: 99%

Smart building trend, role, and position: a systematic literature review

Saputra

Ramadhan²

2023

IJRES

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“…The authors used this method for hydrological time-series data. In their corresponding experiments, they recommend an α value from the interval [0.85, 0.99] and k value from the interval [3,15] This method is a simplification of the previous methods as the coefficients are not fitted by the model like AR, MA, ARMA or other autoregression approaches. It does also not use exponential weights like the ES methods.…”

Section: Arima Modelmentioning

confidence: 99%

“…There have also been different studies comparing ML methods but for a specific sort of data. For instance, Almaguer-Angeles et al [3] compare 22 ML algorithms detecting anomalies on IoT-Datasets. There are also papers where the authors compare their anomaly detection approach with different approaches.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art

Braei,

Wagner

2020

Preprint

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Anomaly detection for time-series data has been an important research field for a long time. Seminal work on anomaly detection methods has been focussing on statistical approaches. In recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. Subsequently, researchers tried to improve these techniques using (deep) neural networks. In the light of the increasing number of anomaly detection methods, the body of research lacks a broad comparative evaluation of statistical, machine learning and deep learning methods. This paper studies 20 univariate anomaly detection methods from the all three categories. The evaluation is conducted on publicly available datasets, which serve as benchmarks for time-series anomaly detection. By analyzing the accuracy of each method as well as the computation time of the algorithms, we provide a thorough insight about the performance of these anomaly detection approaches, alongside some general notion of which method is suited for a certain type of data.

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“…Pero la diferencia radica en que, Extra-tree no realiza muestreo con remplazamiento y los nodos realizan la partición no seleccionando el mejor valor para la división sino empleando un criterio aleatorio, [38]. Extra-Tree se ha empleado para la detección de anomalías en construcciones inteligentes e Internet de las Cosas [39], [40]. 4.…”

Section: Materiales Y Métodosunclassified

Esquemas de combinación de árboles de decisión como estrategia para la detección de anomalías

Quintana-Zaez¹,

Velarde-Bedregal²,

Anton-Vargas³

et al. 2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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Security of the data consumed, generated and stored is crucial to the quality of life in today's society. For this reason, this paper proposes a comparative study of different combination schemes of multiple classifiers based on decision trees, due to its scalability and easy implementation. As a result, precision and recall values of about 97% and 100% were obtained, showing their high reliability, reducing false alarms and high generalization capacity. A comparison with a deep learning based algorithm showed that tree combination strategies are competitive and with statistically similar and superior results to the state-of-the-art. In the end, the results suggest that adaptive strategies such as XGBoost or highly randomized strategies such as Random Forest or Extra-Tree can be alternatives for the protection of precious data on the network.

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Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios

Cited by 11 publications

References 14 publications

Smart building trend, role, and position: a systematic literature review

Smart building trend, role, and position: a systematic literature review

Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art

Esquemas de combinación de árboles de decisión como estrategia para la detección de anomalías

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