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Updated: Jul 16, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

MM-NIDS: A Novel Multimodal Ensemble Fusion Network Intrusion Detection System Using Numeric, Text, Graph, and

Samar AboulEla1, Rasha Kashef1

  • 1Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
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This study introduces MM-NIDS, a multimodal fusion framework for network intrusion detection using NetFlow data. It enhances cybersecurity for IoT and critical infrastructure by improving the detection of evolving cyber threats.

Area of Science:

  • Cybersecurity and Network Engineering
  • Artificial Intelligence and Machine Learning

Background:

  • The proliferation of Internet of Things (IoT) devices increases exposure to sophisticated cyber threats.
  • Traditional intrusion detection systems struggle with resource limitations, evolving attacks, and diverse traffic patterns in modern networks.

Purpose of the Study:

  • To introduce MM-NIDS, a novel multimodal fusion framework for NetFlow-based intrusion detection.
  • To enhance the robustness and adaptability of intrusion detection systems for complex digital environments.

Main Methods:

  • Developed a multimodal fusion framework (MM-NIDS) combining numerical, textual, graph-based, and quantum-inspired NetFlow data representations.
  • Utilized transformer-based architectures (FT-Transformer, ELECTRA-Small) for modeling each data representation.
Keywords:
cybersecuritydeep learninggraph embeddinglanguage modelsmultimodalnetwork intrusion detectionquantum encodingtransformers

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Last Updated: Jul 16, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

  • Integrated predictions using five post hoc fusion strategies, including MLP and XGBoost meta-fusion.

    • Main Results:

    • The text-based model (M2) demonstrated the highest individual performance.
    • Fusion strategies offered modest, dataset-dependent improvements, particularly for underrepresented attacks.
    • NetFlow features proved effective for volumetric and scan-based attacks but less so for stealthy threats.

    Conclusions:

    • MM-NIDS shows potential for deployment in critical infrastructure, industrial IoT, and smart environments.
    • Further research should incorporate semantic or payload-level features to improve detection of evasive threats.