Evaluation of AI-based Malware Detection in IoT Network Traffic

Prazeres, Nuno; Costa, Rogério Luís de C.; Santos, Leonel; Rabadão, Carlos

http://hdl.handle.net/10400.8/8175

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Authors

Prazeres, Nuno

Costa, Rogério Luís de C.

Santos, Leonel

Rabadão, Carlos

Abstract(s)

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.

Description

Artigo publicado em: Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT, 580-585, ISBN 978-989-758-590-6; ISSN 2184-7711, pages 580-585, 2022. DOI: 10.5220/0011279600003283.
A 19ª Conferência Internacional sobre Segurança e Criptografia, foi realizada nos dias 11-13 de julho, de 2022, em Lisboa, Portugal.

Keywords

Internet of Things Machine Learning Intrusion Detection Systems Cybersecurity

URI

http://hdl.handle.net/10400.8/8175

Citation

Prazeres, Nuno, Costa, Rogerio Luís de C., Santos, Leonel, and Rabadao, Carlos. ˜ Evaluation of AI-based Malware Detection in IoT Network Traffic. In Proceedings of the 19th International Conference on Security and Cryptography (SECRYPT 2022), pages 580-585 - DOI: 10.5220/0011279600003283 - ISBN: 978-989- 758-590-6; ISSN: 2184-7711