Multiclass decomposition and Artificial Neural Networks for intrusion detection and identification in Internet of Things environments


The Internet of Things (IoT) systems have limited resources, making it difficult to implement some security mechanisms. It is important to detect attacks against these environments and identify their type. However, existing multi-class detection approaches present difficulties related to false positives and detection of less common attacks. Thus, this work proposes an approach with a two-stage analysis architecture based on One-Vs-All (OVA) and Artificial Neural Networks (ANN) to detect and identify intrusions in fog and IoT computing environments. The results of experiments with the Bot-IoT dataset demonstrate that the approach achieved promising results and reduced the number of false positives compared to state-of-the-art approaches and machine learning techniques.

Palavras-chave: Intrusion Detection, Intrusion Identification, Internet of Things, Fog Computing, One vs. All, Artificial Neural Networks


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DE SOUZA, Cristiano Antonio; CARDOSO, João Vitor; WESTPHALL, Carlos Becker. Multiclass decomposition and Artificial Neural Networks for intrusion detection and identification in Internet of Things environments. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 21. , 2021, Belém. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 85-98. DOI:

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