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A drift-aware RS2FS pipeline with confidence gating for IDS.

Gomathi Sakthivel1, Anitha Kumari Kumarasamy2

  • 1Dr.N.G.P. Institute of Technology, Coimbatore, India. s.gomathi4321@gmail.com.

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|January 10, 2026
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Summary

This study introduces a novel intrusion detection system for Internet of Things (IoT) networks, utilizing a stage-specific feature selection and a cascade classifier to achieve high accuracy and speed. The system effectively identifies various threats with minimal latency, enhancing IoT security.

Keywords:
Adaptive Security ModelCICIoT2023Confidence-gated support vector machineElastic-net screeningInternet of ThingsRecursive sparse & relevance-based feature selection

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Area of Science:

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

Background:

  • Real-time intrusion detection in heterogeneous IoT networks is challenging due to diverse device capabilities, dynamic topologies, and resource constraints.
  • Existing systems often struggle with latency, adaptability, and effective feature selection for complex IoT environments.
  • Lightweight AI, edge processing, and adaptive security models are crucial for efficient IoT network monitoring.

Purpose of the Study:

  • To propose a novel, efficient, and accurate real-time intrusion detection system (IDS) for heterogeneous IoT networks.
  • To develop a stage-specific Recursive Sparse & Relevance-based Feature Selection (RS2FS) mechanism.
  • To implement a confidence-gated Support Vector Machine (SVM) → SVM → Adaptive Neuro-Fuzzy Inference System (ANFIS) cascade classifier.

Main Methods:

  • Employed a stage-specific RS2FS combining elastic-net screening, MI ∩ mRMR relevance, stability selection, and margin-aware recursive pruning for feature reduction.
  • Utilized a confidence-gated SVM→SVM→ANFIS cascade classifier that routes easy cases to SVMs and ambiguous traffic to ANFIS for interpretable decisions.
  • Evaluated the model on the CICIoT2023 dataset using scenario-held-out splits (5× grouped CV) and measured performance metrics including Macro-F1, Macro-AUC, and latency.

Main Results:

  • Achieved high performance with Macro-F1 = 0.962, Macro-AUC = 0.991, and Balanced Accuracy = 0.963 at 6.3 ms CPU latency and a 7.8 MB footprint.
  • Demonstrated significant improvements over baseline models, with a +1.9 pp Macro-F1 increase over SVM-only.
  • The cascade architecture effectively managed the speed-accuracy trade-off, resolving 68% of windows at Stage-1 with high accuracy.

Conclusions:

  • The proposed RS2FS and confidence-gated cascade framework represent a fundamentally new IDS architecture for heterogeneous IoT environments.
  • The system offers accurate, calibrated, interpretable, and fast intrusion detection suitable for real-world IoT deployments.
  • The framework shows robustness against noise, jitter, and missing features, along with effective open-set rejection capabilities.