Evaluation of AI-based Malware Detection in IoT Network Traffic

Nuno Prazeres, Rogério Costa, Leonel Santos, Leonel Santos, Carlos Rabadão, Carlos Rabadão

2022

Abstract

Internet of Things (IoT) devices have become day-to-day technologies. They collect and share a large amount of data, including private data, and are an attractive target of potential attackers. On the other hand, machine learning has been used in several contexts to analyze and classify large volumes of data. Hence, using machine learning to classify network traffic data and identify anomalous traffic and potential attacks promises. In this work, we use deep and traditional machine learning to identify anomalous traffic in the IoT-23 dataset, which contains network traffic from real-world equipment. We apply feature selection and encoding techniques and expand the types of networks evaluated to improve existing results from the literature. We compare the performance of algorithms in binary classification, which separates normal from anomalous traffic, and in multiclass classification, which aims to identify the type of attack.

Paper Citation

in Harvard Style

Prazeres N., Costa R., Santos L. and Rabadão C. (2022). Evaluation of AI-based Malware Detection in IoT Network Traffic. In Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-590-6, pages 580-585. DOI: 10.5220/0011279600003283

in Bibtex Style

@conference{secrypt22,
author={Nuno Prazeres and Rogério Costa and Leonel Santos and Carlos Rabadão},
title={Evaluation of AI-based Malware Detection in IoT Network Traffic},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2022},
pages={580-585},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011279600003283},
isbn={978-989-758-590-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - Evaluation of AI-based Malware Detection in IoT Network Traffic
SN - 978-989-758-590-6
AU - Prazeres N.
AU - Costa R.
AU - Santos L.
AU - Rabadão C.
PY - 2022
SP - 580
EP - 585
DO - 10.5220/0011279600003283