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Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO.

Asima Sarwar1, Abdullah M Alnajim2, Safdar Nawaz Khan Marwat1

  • 1Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan.

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Summary
This summary is machine-generated.

This study introduces an improved dynamic sticky binary particle swarm optimization (IDSBPSO) for feature selection in Internet of Things (IoT) networks. The new method enhances intrusion detection system (IDS) accuracy while reducing computational costs and prediction times.

Keywords:
Internet of ThingsIoT securityanomaly detectionintrusion detection system

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) enables smart environments but introduces significant security and privacy risks due to potential exploitation by attackers.
  • Existing intrusion detection systems (IDS) for IoT face challenges with high-dimensional data, which expands the search space and hinders optimization methods like particle swarm optimization (PSO).

Purpose of the Study:

  • To propose an improved dynamic sticky binary particle swarm optimization (IDSBPSO) algorithm for effective feature selection in IoT networks.
  • To enhance the searchability of existing optimization techniques by introducing dynamic search space reduction and parameters.
  • To design and evaluate an IDS using the proposed IDSBPSO for detecting malicious data traffic in IoT environments.

Main Methods:

  • Developed an improved dynamic sticky binary particle swarm optimization (IDSBPSO) algorithm incorporating dynamic search space reduction and dynamic parameters.
  • Implemented a feature selection approach using IDSBPSO to optimize the input for an intrusion detection system (IDS).
  • Evaluated the performance of the proposed IDS model on two benchmark IoT network datasets: IoTID20 and UNSW-NB15.

Main Results:

  • The IDSBPSO method achieved higher or comparable accuracy in intrusion detection compared to conventional PSO-based feature selection methods, often using fewer features.
  • The proposed IDSBPSO significantly reduced the computational cost and prediction time required for feature selection and intrusion detection.
  • The effectiveness of IDSBPSO in enhancing the searchability of optimization methods for high-dimensional IoT data was demonstrated.

Conclusions:

  • The IDSBPSO algorithm offers an effective solution for feature selection in IoT intrusion detection, balancing accuracy with computational efficiency.
  • The dynamic strategies employed by IDSBPSO successfully address the challenges posed by high-dimensional data in IoT security.
  • This research contributes to the development of more efficient and accurate intrusion detection systems for the Internet of Things.