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Multi-Agent Locally Trained Progressive Instance Selected Assisted Federated Learning Architecture for Intrusion Detection in Industrial-IoT

Author

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  • Divyashree R
  • Sumati Ramakrishna Gowda

Abstract

In this work the focus is made on designing and developing a robust federated learning model (FLM) and architecture for multi-type network intrusion detection system (MT-NIDS) for IIoT applications. Unlike traditional intrusion detection systems, where the key focus is made on detecting and classifying single type of intrusion or attack condition, this research targets to perform multi-type network intrusion detection. This as a result can contribute a fit-to-all NIDS solution for IIoT environment.

Suggested Citation

  • Divyashree R & Sumati Ramakrishna Gowda, 2024. "Multi-Agent Locally Trained Progressive Instance Selected Assisted Federated Learning Architecture for Intrusion Detection in Industrial-IoT," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(6), pages 500-506, November.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i6:id:440
    DOI: 10.32628/CSEIT24106190
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24106190
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