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A distributed SDN-based intrusion detection system for IoT using optimized forests.

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Summary
This summary is machine-generated.

This study introduces a novel distributed intrusion detection system (IDS) for the Internet of Things (IoT) using software-defined networking (SDN). The system enhances security by optimizing decision trees with a black hole optimization (BHO) algorithm for accurate attack detection.

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Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • The proliferation of the Internet of Things (IoT) necessitates advanced security measures.
  • Existing intrusion detection systems (IDS) often lack architecture-specific optimizations for evolving IoT networks.
  • A need exists for efficient and accurate intrusion detection systems tailored for distributed IoT environments.

Purpose of the Study:

  • To propose a novel distributed intrusion detection system (IDS) leveraging Software-Defined Networking (SDN) architecture for enhanced IoT security.
  • To improve the accuracy and efficiency of intrusion detection within sub-networks using an optimized decision tree approach.
  • To evaluate the performance of the proposed IDS against established datasets.

Main Methods:

  • A distributed intrusion detection system (IDS) was designed based on Software-Defined Networking (SDN) principles.
  • The network was segmented into sub-networks managed by controller nodes for localized intrusion detection.
  • Decision trees for intrusion detection were optimized using the Black Hole Optimization (BHO) algorithm, focusing on pruning and split point determination for maximized accuracy.

Main Results:

  • The proposed SDN-based IDS demonstrated high accuracy in detecting network attacks.
  • Performance evaluation on the NSLKDD dataset yielded an accuracy of 99.2%.
  • Evaluation on the NSW-NB15 dataset achieved an accuracy of 97.2%, surpassing previous methods.

Conclusions:

  • The developed distributed intrusion detection system effectively enhances IoT network security.
  • The integration of SDN and BHO-optimized decision trees provides a robust and accurate solution for identifying cyber threats.
  • The proposed method offers a significant improvement over existing intrusion detection techniques in terms of accuracy and efficiency.