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Related Experiment Videos

Federated sparse-learning framework for detecting low-rate stealth attacks in resource-constrained IoT devices.

R Anandhi1, G Umarani Srikanth2

  • 1Department of Information Technology, Panimalar Engineering College, Chennai, India. rajamala2311@gmail.com.

Scientific Reports
|May 9, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces FSL-IDS, a novel intrusion detection framework for IoT devices, significantly enhancing the detection of low-rate stealth attacks. The system achieves high accuracy by integrating sparse learning, temporal modeling, and federated optimization, outperforming existing methods.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • Low-rate stealth attacks in IoT environments are challenging due to their evasive traffic patterns.
  • Traditional intrusion detection systems struggle to identify these slow, irregular, and noise-like attacks.
  • Resource constraints in IoT deployments necessitate efficient and distributed detection solutions.

Purpose of the Study:

  • To propose FSL-IDS, a hierarchical federated intrusion detection framework for resource-constrained IoT environments.
  • To enhance the detection capabilities for low-rate stealth attacks in IoT networks.
  • To enable efficient deployment and operation of intrusion detection systems on edge devices.

Main Methods:

  • Developed a hierarchical federated intrusion detection framework (FSL-IDS) integrating sparse representation learning, hierarchical temporal modeling, and federated optimization.
Keywords:
Internet of things (IoT)Machine learningSecuritylightweight protection

Related Experiment Videos

  • Implemented a sparsity-constrained encoder at the device level, a Hierarchical Ensemble for Correlated Events (H-EFCE) at fog nodes, and a Federated Gradient Aggregation Module (FGAM) at the cloud layer.
  • Utilized a Synthetic Stealth Attack Injection Module (SSAIM) for data augmentation and a Lightweight Quantized Model Optimization Layer (LQMOL) for efficient deployment.

    • Main Results:

    • The FSL-IDS framework significantly improved stealth attack detection compared to baseline methods on the IoTID20 dataset.
    • The FGAM-based cloud model achieved an AUC-ROC of 0.992 and an F1-score of 0.965.
    • Ablation studies confirmed the contribution of each component, and model compression demonstrated 43% energy savings with 6-bit quantization while maintaining performance.

    Conclusions:

    • FSL-IDS effectively addresses the challenge of detecting low-rate stealth attacks in IoT environments.
    • The hierarchical federated approach combined with sparse learning and temporal modeling offers superior performance.
    • Quantized model optimization enables efficient and secure deployment on resource-constrained IoT devices.