Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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Comparison of Machine Learning Models for IoT Malware Classification

Authors
Piragash Maran1, *, Timothy Tzen Vun Yap1, Ji Jian Chin1, Hu Ng1, Vik Tor Goh2, Thiam Yong Kuek3
1Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
2Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Malaysia
3Faculty of Business and Finance, Universiti Tunku Abdul Rahman, 31900, Kampar, Malaysia
*Corresponding author. Email: 1181101448@student.mmu.edu.my
Corresponding Author
Piragash Maran
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-094-7_3How to use a DOI?
Keywords
Machine learning; Cybersecurity; Internet of Things; IoT-23; Malware classification; Malware analysis
Abstract

The Internet of Things (IoT) is a system where devices and sensors are interconnected to improve accuracy, efficiency, precision and consistency. It is being developed rapidly as more people are aware of this system. From farmers, all the way to the automotive engineers are all benefiting from the usage of IoT (Internet of Things). IoT transfers data in a very large amount without the help of a human, making the system very efficient and time saving. Since there is no assistance from humans, IoT can generate more data than ever. This paper focuses more on the security part of IoT devices or sensors. Machine learning (ML) algorithms are used to investigate and detect any malware in a dataset generated from an IoT device. The paper concludes which algorithm is more successful in detecting malware from the dataset and compares the result or the accuracy. The algorithms that this paper used are Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Decision Tree (DT) and K-Nearest Neighbours (KNN). The best results were achieved by the Random Forest algorithm with an accuracy score of 96%.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
27 December 2022
ISBN
978-94-6463-094-7
ISSN
2589-4900
DOI
10.2991/978-94-6463-094-7_3How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Piragash Maran
AU  - Timothy Tzen Vun Yap
AU  - Ji Jian Chin
AU  - Hu Ng
AU  - Vik Tor Goh
AU  - Thiam Yong Kuek
PY  - 2022
DA  - 2022/12/27
TI  - Comparison of Machine Learning Models for IoT Malware Classification
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 15
EP  - 28
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_3
DO  - 10.2991/978-94-6463-094-7_3
ID  - Maran2022
ER  -