Machine Learning to Automate Network Segregation for Enhanced Security in Industry 4.0

The heavy reliance of Industry 4.0 on emerging communication technologies, notably Industrial Internet-of-Things (IIoT) and Machine-Type Communications (MTC), and the increasing exposure of these traditionally isolated infrastructures to the Internet, are tremendously increasing the attack surface. Network segregation is a viable solution to address this problem. It essentially splits the network into several logical groups (subnetworks) and enforces adequate security policy on each segment, e.g., restricting unnecessary intergroup communications or controlling the access. However, existing segregation techniques primarily depend on manual configurations, which renders them inefficient for cyber-physical production systems because they are highly complex and heterogeneous environments with massive number of communicating machines. In this paper, we incorporate machine learning to automate network segregation, by efficiently classifying network end-devices into several groups through examining the traffic patterns that they generate. For performance evaluation, we analysed the data collected from a large segment of Infineon’s network in the context of the EU funded ECSEL-JU project “SemI40”. In particular, we applied feature selection and trained several supervised learning algorithms. Test results, using 10-fold cross validation, revealed that the algorithms generalise very well and achieve an accuracy up to 99.4%

Algoritmos de machine learning y su aplicación al mantenimiento industrial en el sector agroalimentario

Las aplicaciones de Machine Learning, o aprendizaje automático, son soluciones que, tras su implementación, continúan mejorando con el tiempo y con una mínima intervención humana, lo que las hace muy adecuadas para ayudar en las labores de mantenimiento de cualquier industria. Se han analizado 10 algoritmos, de los más utilizados, para comprender los conceptos básicos del aprendizaje automático, los problemas que solucionan y seleccionar el mejor algoritmo para la aplicación al mantenimiento predictivo en una industria agroalimentaria española: Solán de Cabras