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A Lightweight Intelligent Network Intrusion Detection System Using One-Class Autoencoder and Ensemble Learning for

Wenbin Yao1, Longcan Hu2, Yingying Hou2

  • 1School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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Summary

This study introduces a novel network intrusion detection system (NIDS) for IoT cybersecurity. The system effectively identifies both known and unknown cyber threats using a Bidirectional GRU Autoencoder and ensemble learning.

Keywords:
IoTbidirectional GRU autoencoderintrusion detectionnovelty detectionone-class classification

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Area of Science:

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

Background:

  • Traditional network intrusion detection systems struggle with unknown (zero-day) attacks.
  • Retraining models for new threats is slow and requires expert intervention.
  • Existing systems lack efficiency in identifying novel attack patterns in IoT environments.

Purpose of the Study:

  • To develop a lightweight, intelligent network intrusion detection system (NIDS) for enhanced IoT cybersecurity.
  • To accurately identify normal, abnormal, and unknown network traffic.
  • To improve the detection rate of novel cyber threats without constant retraining.

Main Methods:

  • Implementation of a One-Class Bidirectional GRU Autoencoder model trained on normal network data.
  • Application of ensemble learning with Soft Voting for robust classification of network traffic.
  • Validation using benchmark datasets: WSN-DS, UNSW-NB15, and KDD CUP99.

Main Results:

  • Achieved high recognition rates: 97.91% on WSN-DS, 98.92% on UNSW-NB15, and 98.23% on KDD CUP99.
  • The proposed model accurately distinguishes normal from abnormal data, including unknown attacks.
  • Effectively classifies unknown attacks by identifying the most similar known attack type.

Conclusions:

  • The proposed lightweight intelligent NIDS demonstrates high feasibility and efficiency for IoT cybersecurity.
  • The combination of One-Class Bidirectional GRU Autoencoder and ensemble learning offers superior performance in detecting known and unknown threats.
  • The algorithm's portability across different datasets confirms its practical applicability.