Detection of Mirai Malware Attacks in IoT Environments Using Random Forest Algorithms

Detection of Mirai Malware Attacks in IoT Environments Using Random Forest Algorithms

The potential for Cyber-attacks against Internet of Thing (IoT) Infrastructure is enormous as devices run on pre-existing network infrastructure, for example Mirai Malware Attack. Network Forensics investigations require the Random Forest Algorithm which is used to perform classification and detecti...

Full description

Bibliographic Details
Main Authors: Nur Widiyasono, Ida Ayu Dwi Giriantari, Made Sudarma, L Linawati
Format: Article
Language:English
Published: UIKTEN 2021-08-01
Series:TEM Journal
Subjects:
iot
mirai malware
random forest algorithm
iot environment
Online Access:https://www.temjournal.com/content/103/TEMJournalAugust2021_1209_1219.pdf
id doaj-85ee012535714e5aa526a7854bff4e9a
record_format Article
spelling doaj-85ee012535714e5aa526a7854bff4e9a2021-09-01T12:32:33ZengUIKTENTEM Journal2217-83092217-83332021-08-011031209121910.18421/TEM103-27Detection of Mirai Malware Attacks in IoT Environments Using Random Forest AlgorithmsNur WidiyasonoIda Ayu Dwi GiriantariMade SudarmaL LinawatiThe potential for Cyber-attacks against Internet of Thing (IoT) Infrastructure is enormous as devices run on pre-existing network infrastructure, for example Mirai Malware Attack. Network Forensics investigations require the Random Forest Algorithm which is used to perform classification and detection techniques for the Mirai Malware attack. The trials have been carried out using 5 attack scenarios and device types. The experimental results show that the RF algorithm achieves optimal performance with an average accuracy value of 95.01%, recall 90.82%, F1 Score 93.85% and the best precision value 99.23%. Besides, the Random Forest algorithm is suitable for very large data processing. The contribution of this research is to provide a recommendation that the RF Algorithm can be used to classify and identify Mirai malware attacks on the Internet of Things infrastructure.https://www.temjournal.com/content/103/TEMJournalAugust2021_1209_1219.pdfiotmirai malwarerandom forest algorithmiot environment
collection DOAJ
language English
format Article
sources DOAJ
author Nur Widiyasono
Ida Ayu Dwi Giriantari
Made Sudarma
L Linawati
spellingShingle Nur Widiyasono
Ida Ayu Dwi Giriantari
Made Sudarma
L Linawati
Detection of Mirai Malware Attacks in IoT Environments Using Random Forest Algorithms
TEM Journal
iot
mirai malware
random forest algorithm
iot environment
author_facet Nur Widiyasono
Ida Ayu Dwi Giriantari
Made Sudarma
L Linawati
author_sort Nur Widiyasono
title Detection of Mirai Malware Attacks in IoT Environments Using Random Forest Algorithms
title_short Detection of Mirai Malware Attacks in IoT Environments Using Random Forest Algorithms
title_full Detection of Mirai Malware Attacks in IoT Environments Using Random Forest Algorithms
title_fullStr Detection of Mirai Malware Attacks in IoT Environments Using Random Forest Algorithms
title_full_unstemmed Detection of Mirai Malware Attacks in IoT Environments Using Random Forest Algorithms
title_sort detection of mirai malware attacks in iot environments using random forest algorithms
publisher UIKTEN
series TEM Journal
issn 2217-8309
2217-8333
publishDate 2021-08-01
description The potential for Cyber-attacks against Internet of Thing (IoT) Infrastructure is enormous as devices run on pre-existing network infrastructure, for example Mirai Malware Attack. Network Forensics investigations require the Random Forest Algorithm which is used to perform classification and detection techniques for the Mirai Malware attack. The trials have been carried out using 5 attack scenarios and device types. The experimental results show that the RF algorithm achieves optimal performance with an average accuracy value of 95.01%, recall 90.82%, F1 Score 93.85% and the best precision value 99.23%. Besides, the Random Forest algorithm is suitable for very large data processing. The contribution of this research is to provide a recommendation that the RF Algorithm can be used to classify and identify Mirai malware attacks on the Internet of Things infrastructure.
topic iot
mirai malware
random forest algorithm
iot environment
url https://www.temjournal.com/content/103/TEMJournalAugust2021_1209_1219.pdf
work_keys_str_mv AT nurwidiyasono detectionofmiraimalwareattacksiniotenvironmentsusingrandomforestalgorithms
AT idaayudwigiriantari detectionofmiraimalwareattacksiniotenvironmentsusingrandomforestalgorithms
AT madesudarma detectionofmiraimalwareattacksiniotenvironmentsusingrandomforestalgorithms
AT llinawati detectionofmiraimalwareattacksiniotenvironmentsusingrandomforestalgorithms
_version_ 1721182679326523392

Similar Items