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IR Frequency Region: Fingerprint Region01:03

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion

G Logeswari1, K Thangaramya2, M Selvi3

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

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|March 7, 2025
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Summary

This study introduces a novel Intrusion Detection System (IDS) for Internet of Things (IoT) networks. The proposed system, using Synergistic Dual-Layer Feature Selection (SDFC), significantly improves the detection of cyber threats in IoT environments.

Keywords:
Anomaly detectionDynamic feature selectionInternet of thingsIntrusion detection systemMachine learningSecurity systems

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Area of Science:

  • Cybersecurity
  • Network Security
  • Internet of Things (IoT)

Background:

  • Internet of Things (IoT) networks face escalating cyber threats.
  • Traditional security methods are insufficient for unique IoT vulnerabilities.
  • A specialized Intrusion Detection System (IDS) is crucial for IoT security.

Purpose of the Study:

  • To develop and evaluate a novel IDS tailored for IoT environments.
  • To enhance the detection accuracy and efficiency of identifying malicious network traffic.
  • To address the unique security challenges posed by interconnected IoT devices.

Main Methods:

  • Implemented a multi-subsystem IDS: data pre-processing, feature selection (Synergistic Dual-Layer Feature Selection - SDFC), and classification.
  • SDFC combined statistical methods (mutual information, variance thresholding) with model-based techniques (SVM-RFE, PSO).
  • Utilized a two-stage classifier (LightGBM and XGBoost) on the TON-IoT dataset.

Main Results:

  • The proposed SDFC method significantly improved classifier performance.
  • Achieved higher accuracy, precision, recall, and F1 scores compared to existing methods.
  • Demonstrated effective identification of normal versus malicious network traffic.

Conclusions:

  • The developed IDS, featuring the SDFC algorithm, offers a robust solution for IoT network security.
  • The multi-faceted approach effectively enhances threat detection capabilities in complex IoT ecosystems.
  • The system provides a significant advancement in securing interconnected devices against cyber threats.