IoT DDoS Detection Based on Stream Learning

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Arbex, Gustavo Vitral ; Machado, Ketly Goncalves ; Nogueira, Michele ; Batista, Daniel M. ; Hirata, Roberto, Jr. ; Machuca, CM ; Martins, L ; Sargento, S ; Wauters, T ; Jorge, L ; Salhab, N ; Chemouil, P

• Tipo de documento: Artigo Científico
• Fonte: PROCEEDINGS OF THE 2021 12TH INTERNATIONAL CONFERENCE ON NETWORK OF THE FUTURE (NOF 2021); v. N/A, p. 8-pg., 2021-01-01.

Resumo

The Internet of Things (IoT) represents a new reality, as smart devices spread quickly and a higher number of applications arises. This attracts the attention of not only legitimate users but also attackers aiming to jeopardize the entire IoT infrastructure. Intrusion detection mechanisms are paramount in this networking environment as its first line of defense. Hence, this work proposes a Network Intrusion Detection System (NIDS) that deals with the Distributed Denial of Service (DDoS) attack, one of the most critical attacks that occur through IoT. The proposed NIDS uses stream learning to detect DDoS attacks in the IoT network and is designed to be deployed in a fog infrastructure. The detection model, built on Hoeffding Anytime Tree (HATT) algorithm, achieved a 99% accuracy and a 99% recall. (AU)

• Processo FAPESP: 14/50937-1 - INCT 2014: da Internet do Futuro
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 15/24485-9 - Internet do futuro aplicada a cidades inteligentes
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/22979-2 - IoT-SED: segurança e eficiência no transporte de dados na Internet das Coisas
• Beneficiário: Daniel Macêdo Batista
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 18/23098-0 - MENTORED: da modelagem à experimentação - predizendo e detectando ataques DDoS e zero-day
• Beneficiário: Michele Nogueira Lima
• Modalidade de apoio: Auxílio à Pesquisa - Temático