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Related Experiment Videos

Efficient abnormal behavior detection in information-centric internet of things using SVM.

Nahideh Derakhshanfard1, Tala Salimzadeh2, Amir Mollanejad3

  • 1Department of Computer Engineering, Ta.C, Islamic Azad University, Tabriz, Iran. N.derakhshan@iaut.ac.ir.

Scientific Reports
|May 27, 2026
PubMed
Summary

This study introduces a Support Vector Machine (SVM) model for detecting abnormal behavior in information-centric Internet of Things networks. The SVM model enhances network security and stability by accurately classifying node activities.

Keywords:
Energy OptimizationInformation-Centric IoTSupport Vector MachinesTraffic Patterns

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Area of Science:

  • Computer Science
  • Network Engineering
  • Data Science

Background:

  • Information-centric Internet of Things (IoT) networks face challenges with abnormal traffic due to complex interactions and diverse applications.
  • Existing methods for traffic analysis often lack accuracy, degrade with traffic changes, and are computationally intensive for resource-constrained IoT nodes.

Purpose of the Study:

  • To develop an efficient and accurate model for predicting and classifying normal versus abnormal node behavior in information-centric IoT networks.
  • To address the limitations of existing methods, particularly their computational demands and performance under dynamic traffic conditions.

Main Methods:

  • A Support Vector Machine (SVM) based model was developed to classify node behavior.
  • The model utilizes features such as packet reception rate, delay, and energy consumption for classification.
  • Evaluation was performed on the MDC and UNSW-NB15 datasets.

Main Results:

  • The proposed SVM model demonstrated improved accuracy by 12% and 9% compared to Support Vector Regression (SVR) and SVM with denoising, respectively.
  • An enhanced F1-score of 15% was achieved, indicating better classification performance.
  • The model maintained stable performance even with increased network delay and packet load.

Conclusions:

  • The SVM-based model offers an efficient and accurate solution for abnormal behavior detection in information-centric IoT networks.
  • The model's performance and stability contribute to optimizing energy usage and maintaining network integrity.
  • Statistical significance of performance improvements was confirmed through Wilcoxon and Friedman tests.