SIMBIoTA-ML: Light-weight, Machine Learning-based Malware Detection for Embedded IoT Devices

Dorottya Papp, Gergely Ács, Roland Nagy, Levente Buttyán, Levente Buttyán

2022

Abstract

Embedded devices are increasingly connected to the Internet to provide new and innovative applications in many domains. However, these devices can also contain security vulnerabilities, which allow attackers to compromise them using malware. In this paper, we present SIMBIoTA-ML, a light-weight antivirus solution that enables embedded IoT devices to take advantage of machine learning-based malware detection. We show that SIMBIoTA-ML can respect the resource constraints of embedded IoT devices, and it has a true positive malware detection rate of ca. 95%, while having a low false positive detection rate at the same time. In addition, the detection process of SIMBIoTA-ML has a near-constant running time, which allows IoT developers to better estimate the delay introduced by scanning a file for malware, a property that is advantageous in real-time applications, notably in the domain of cyber-physical systems.

Paper Citation

in Harvard Style

Papp D., Ács G., Nagy R. and Buttyán L. (2022). SIMBIoTA-ML: Light-weight, Machine Learning-based Malware Detection for Embedded IoT Devices. In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-564-7, pages 55-66. DOI: 10.5220/0011080200003194

in Bibtex Style

@conference{iotbds22,
author={Dorottya Papp and Gergely Ács and Roland Nagy and Levente Buttyán},
title={SIMBIoTA-ML: Light-weight, Machine Learning-based Malware Detection for Embedded IoT Devices},
booktitle={Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2022},
pages={55-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011080200003194},
isbn={978-989-758-564-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - SIMBIoTA-ML: Light-weight, Machine Learning-based Malware Detection for Embedded IoT Devices
SN - 978-989-758-564-7
AU - Papp D.
AU - Ács G.
AU - Nagy R.
AU - Buttyán L.
PY - 2022
SP - 55
EP - 66
DO - 10.5220/0011080200003194