Analysis of machine learning algorithms for anomaly detection on edge devices

Aleks Huč (Author), Jakob Šalej (Author), Mira Trebar (Author)

Abstract

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.

Keywords

strojno učenje;klasifikacija;robno računanje;neuravnotežena podatkovna zbirka;učna zbirka;zaznavanje anomalij;gručenje;machine learning;classification;edge computing;imbalanced dataset;training dataset;anomaly detection;clustering;

Data

Language:	English
Year of publishing:	2021
Typology:	1.01 - Original Scientific Article
Organization:	UL FRI - Faculty of Computer and Information Science
UDC:	004.85
COBISS:	71000323
ISSN:	1424-8220
Views:	112
Downloads:	48
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	strojno učenje;klasifikacija;robno računanje;neuravnotežena podatkovna zbirka;učna zbirka;zaznavanje anomalij;gručenje;
Type (COBISS):	Article
Pages:	str. 1-23
Volume:	ǂVol. ǂ21
Issue:	ǂno. ǂ14
Chronology:	Jul. 2021
DOI:	10.3390/s21144946
ID:	14889982