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Article

Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices

Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, SI-1000 Ljubljana, Slovenia
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Academic Editor: Juan M. Corchado
Sensors 2021, 21(14), 4946; https://doi.org/10.3390/s21144946
Received: 1 June 2021 / Revised: 11 July 2021 / Accepted: 16 July 2021 / Published: 20 July 2021
(This article belongs to the Special Issue Edge-Based AI for the Internet of Things)
The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to central systems or the cloud for analysis. Lately, the idea of moving intelligence to the IoT is becoming feasible, with machine learning (ML) moved to edge devices. The aim of this study is to provide an experimental analysis of processing a large imbalanced dataset (DS2OS), split into a training dataset (80%) and a test dataset (20%). The training dataset was reduced by randomly selecting a smaller number of samples to create new datasets Di (i = 1, 2, 5, 10, 15, 20, 40, 60, 80%). Afterwards, they were used with several machine learning algorithms to identify the size at which the performance metrics show saturation and classification results stop improving with an F1 score equal to 0.95 or higher, which happened at 20% of the training dataset. Further on, two solutions for the reduction of the number of samples to provide a balanced dataset are given. In the first, datasets DRi consist of all anomalous samples in seven classes and a reduced majority class (‘NL’) with i = 0.1, 0.2, 0.5, 1, 2, 5, 10, 15, 20 percent of randomly selected samples. In the second, datasets DCi are generated from the representative samples determined with clustering from the training dataset. All three dataset reduction methods showed comparable performance results. Further evaluation of training times and memory usage on Raspberry Pi 4 shows a possibility to run ML algorithms with limited sized datasets on edge devices. View Full-Text
Keywords: machine learning; classification; edge computing; imbalanced dataset; training dataset; anomaly detection; clustering machine learning; classification; edge computing; imbalanced dataset; training dataset; anomaly detection; clustering
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MDPI and ACS Style

Huč, A.; Šalej, J.; Trebar, M. Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices. Sensors 2021, 21, 4946. https://doi.org/10.3390/s21144946

AMA Style

Huč A, Šalej J, Trebar M. Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices. Sensors. 2021; 21(14):4946. https://doi.org/10.3390/s21144946

Chicago/Turabian Style

Huč, Aleks, Jakob Šalej, and Mira Trebar. 2021. "Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices" Sensors 21, no. 14: 4946. https://doi.org/10.3390/s21144946

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