SafeMan: A unified framework to manage cybersecurity and safety in manufacturing industry

Ángel Luis Perales Gómez, Lorenzo Fernández Maimó, Alberto Huertas Celdrán, Félix J. García Clemente, Manuel Gil Pérez, Gregorio Martínez Pérez

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Industrial control systems (ICS) are considered cyber-physical systems that join both cyber and physical worlds. Due to their tight interaction, where humans and robots co-work and co-inhabit in the same workspaces and production lines, cyber-attacks targeting ICS can alter production processes and even bypass safety procedures. As an example, these cyber-attacks could interrupt physical industrial processes and cause potential injuries to workers. In this article, we present SafeMan, a unified management framework based on the Edge Computing paradigm that provides high-performance applications for the detection and mitigation of both cyber-attacks and safety threats in industrial scenarios. Three use cases show specific threats in manufacturing as well as the SafeMan actions carried out to detect and mitigate them. In order to validate our proposal, a pool of experiments was performed with Electra, an industrial dataset with normal network traffic and different cyber-attacks by using a given number of Modbus TCP and S7Comm devices. The experiments measured the runtime performance of anomaly detection techniques based on machine learning and deep learning to detect cyber-attacks in control networks. The experimental results show that Neural Networks report the best performance, being able to examine 217 feature vectors per second over Electra, and therefore demonstrating that it can be used as detection model for SafeMan in real scenarios.

Original languageEnglish
Pages (from-to)607-627
Number of pages21
JournalSoftware - Practice and Experience
Volume51
Issue number3
DOIs
Publication statusPublished - Mar 2021

Keywords

  • anomaly detection
  • cybersecurity
  • deep learning
  • industrial control system
  • machine learning
  • safety

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