SIMBIoTA: Similarity-based Malware Detection on IoT Devices

Csongor Tamás, Csongor Tamás, Dorottya Papp, Levente Buttyán

2021

Abstract

Embedded devices connected to the Internet are threatened by malware, and currently, no antivirus product is available for them. We present SIMBIoTA, a new approach for detecting malware on such IoT devices. SIMBIoTA relies on similarity-based malware detection, and it has a number of notable advantages: moderate storage requirements on resource constrained IoT devices, a fast and lightweight malware detection process, and a surprisingly good detection performance, even for new, never-before-seen malware. These features make SIMBIoTA a viable antivirus solution for IoT devices, with competitive detection performance and limited resource requirements.

Paper Citation

in Harvard Style

Tamás C., Papp D. and Buttyán L. (2021). SIMBIoTA: Similarity-based Malware Detection on IoT Devices. In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-504-3, pages 58-69. DOI: 10.5220/0010441500580069

in Bibtex Style

@conference{iotbds21,
author={Csongor Tamás and Dorottya Papp and Levente Buttyán},
title={SIMBIoTA: Similarity-based Malware Detection on IoT Devices},
booktitle={Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2021},
pages={58-69},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010441500580069},
isbn={978-989-758-504-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - SIMBIoTA: Similarity-based Malware Detection on IoT Devices
SN - 978-989-758-504-3
AU - Tamás C.
AU - Papp D.
AU - Buttyán L.
PY - 2021
SP - 58
EP - 69
DO - 10.5220/0010441500580069