OMINACS: Online ML-Based IoT Network Attack Detection and Classification System

Author(s)

Abreu, Diego ; Abelem, Antonio ; Moraes, IM ; Campista, MEM ; Ghamri-Doudane, Y ; Costa, LHMK ; Rubinstein, MG

• Document type: Journal article
• Source: 2022 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS (LATINCOM); v. N/A, p. 6-pg., 2022-01-01.

Abstract

Several Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents. However, detecting and classifying attacks with high accuracy and precision is still a major challenge. This paper proposes an online attack detection and network traffic classification system, which combines stream Machine Learning, Deep Learning, and Ensemble Learning technique. Using multiple stages of data analysis, the system can detect the presence of malicious traffic flows and classify them according to the type of attack they represent. Furthermore, we show how to implement this system both in an IoT network and from an ML point of view. The system was evaluated in three IoT network security datasets, in which it obtained accuracy and precision above 90% with a reduced false alarm rate. (AU)

• FAPESP's process: 20/04031-1 - Quantum internet routing protocols
• Grantee: Antonio Jorge Gomes Abelém
• Support Opportunities: Regular Research Grants