Full Program
Summary:
The rapid adoption of Industry 4.0/5.0 and Industrial Internet of Things (IIoT) has introduced new cybersecurity vulnerabilities in industrial operations. IIoT devices despite their widespread deployment face resource constraints that make traditional security solutions ineffective. While Machine Learning (ML)-based Intrusion Detection Systems (IDS) have emerged as promising solutions they often face limitations when dealing with industrial-scale data sets and multi-class attack detection scenarios. This paper proposes an enhanced weighted ensemble learning approach that overcomes these limitations while maintaining robust intrusion detection capabilities in IIoT and Edge Computing environments. Our solution features a weighted method that processes data into strategic subsets creating an optimized ensemble of ML models. The model integrates data collection preprocessing optimization model training and evaluation phases. Our approach validated on the Edge-IIoTset public dataset yielded impressive results: the TabPFN model achieved a 99.81\% F1-Score and all other implemented models demonstrated significant performance improvements over their respective baseline.Author(s):
Sergio Ruiz-Villafranca
Universidad de Castilla-La Mancha
Spain
Luis Miguel Garcia-Sáez
Universidad de Castilla-La Mancha
Spain
José Roldán-Gómez
Universidad de Zaragoza
Spain
Javier Carrillo-Mondéjar
Universidad de Zaragoza
Spain
Juan Manuel Castelo Gómez
Universidad Politécnica de Madrid
Spain
Jose Luis Martínez
Universidad de Castilla-La Mancha
Spain