A Modular AI-Driven Intrusion Detection System for Network Traffic Monitoring in Industry 4.0, Using Nvidia Morpheus and Generative Adversarial Networks

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 1; p. 130
• Main Authors: Chiriac, Beatrice-Nicoleta, Anton, Florin-Daniel, Ioniță, Anca-Daniela, Vasilică, Bogdan-Valentin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2025; MDPI
• Subjects: Algorithms; Analysis; Artificial intelligence; Classification; Cybersecurity; Datasets; Deep learning; Detectors; Efficiency; event monitoring; Flexibility; Industrial Internet of Things; Industry 4.0; Information management; Infrastructure; Internet of Things (IoT); intrusion detection and protection system; Intrusion detection systems; Machine learning; networking; Neural networks; Open source software; Performance evaluation; Security software; Internet of Things (IoT); neural networks; event monitoring; Industry 4.0; networking; intrusion detection and protection system; artificial intelligence
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25010130

Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, and intrusion detection systems (IDSs) based on artificial intelligence (AI) algorithms are coming up with an answer to this critical issue. This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.0 by integrating the computational advantages of the Nvidia Morpheus open-source AI framework. The solution is modularly built with two pipelines for data analysis. The pipelines use a pre-trained XGBoost (eXtreme Gradient Boosting) model that achieved an accuracy score of up to 90%. The proposed IDS has a fast rate of analysis, managing more than 500,000 inputs in almost 10 s, due to the application of the federated learning methodology. The classification performance of the model was improved by integrating a generative adversarial network (GAN) that generates polymorphic network traffic packets.