Analysis of machine learning algorithms for anomaly detection on edge devices

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

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.

• Jezik: Angleški jezik
• Ključne besede: machine learning, classification, edge computing, imbalanced dataset, training dataset, anomaly detection, clustering
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2021
• Št. strani: 22 str.
• Številčenje: Vol. 21, iss. 14, art. 4946
• PID: 20.500.12556/RUL-135739
• UDK: 004.85
• ISSN pri članku: 1424-8220
• DOI: 10.3390/s21144946
• COBISS.SI-ID: 71000323
• Datum objave v RUL: 30.03.2022

Gradivo je del revije

• Naslov: Sensors
• Skrajšan naslov: Sensors
• Založnik: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 20.07.2021

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, klasifikacija, robno računanje, neuravnotežena podatkovna zbirka, učna zbirka, zaznavanje anomalij, gručenje

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0359
• Naslov: Vseprisotno računalništvo