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Evaluation of AI-based Malware Detection in IoT Network Traffic
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Orientador(es)
Resumo(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.
Descrição
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.
A 19ª Conferência Internacional sobre Segurança e Criptografia, foi realizada nos dias 11-13 de julho, de 2022, em Lisboa, Portugal.
Palavras-chave
Internet of Things Machine Learning Intrusion Detection Systems Cybersecurity
Contexto Educativo
Citação
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
Editora
Science and Technology Publications