Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning

Autor(es)

Zolanvari, Maede ; Teixeira, Marcio A. ; Jain, Raj ; Lee, D ; Saxena, N ; Kumaraguru, P ; Mezzour, G

• Tipo de documento: Artigo Científico
• Fonte: 2018 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI); v. N/A, p. 6-pg., 2018-01-01.

Resumo

Machine learning algorithms have been shown to be suitable for securing platforms for IT systems. However, due to the fundamental differences between the industrial internet of things (IIoT) and regular IT networks, a special performance review needs to be considered. The vulnerabilities and security requirements of IIoT systems demand different considerations. In this paper, we study the reasons why machine learning must be integrated into the security mechanisms of the IIoT, and where it currently falls short in having a satisfactory performance. The challenges and real-world considerations associated with this matter are studied in our experimental design. We use an IIoT testbed resembling a real industrial plant to show our proof of concept. (AU)

• Processo FAPESP: 17/01055-4 - Plataforma de gerenciamento, implantação e distribuição de aplicações em ambiente multi-cloud
• Beneficiário: Marcio Andrey Teixeira
• Modalidade de apoio: Bolsas no Exterior - Pesquisa