Comparison of Machine Learning Models for IoT Malware Classification
- 10.2991/978-94-6463-094-7_3How to use a DOI?
- Machine learning; Cybersecurity; Internet of Things; IoT-23; Malware classification; Malware analysis
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%.
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Cite this article
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 -