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Towards an Effective Intrusion Detection Model Using Focal Loss Variational Autoencoder for Internet of Things (IoT).

Shapla Khanam1, Ismail Ahmedy1,2, Mohd Yamani Idna Idris1,2

  • 1Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia.

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Summary

This study introduces a Class-wise Focal Loss Variational AutoEncoder (CFLVAE) to address imbalanced network traffic for intrusion detection systems. The novel model generates realistic attack data, significantly improving detection accuracy and identifying rare attacks in the Internet of Things.

Keywords:
Class-wise Focal LossDeep Neural NetworkInternet of ThingsVariational AutoEncoderdata imbalanceintrusion detection

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • Intrusion detection systems (IDS) are critical for securing diverse network applications, especially the Internet of Things (IoT), due to its high volume of sensitive data.
  • Traditional learning-based IDS struggle with imbalanced network traffic, leading to poor performance, high false positives, and missed minority-class attacks.
  • Existing data generation methods often fail to produce diverse and realistic samples for underrepresented attack classes.

Purpose of the Study:

  • To propose a novel deep generative model, the Class-wise Focal Loss Variational AutoEncoder (CFLVAE), to overcome data imbalance in intrusion detection.
  • To develop an effective, cost-sensitive objective function, Class-wise Focal Loss (CFL), to enhance Variational AutoEncoder (VAE) performance in generating synthetic intrusion data.
  • To improve the accuracy and effectiveness of intrusion detection classifiers by training them on a balanced dataset generated by the CFLVAE.

Main Methods:

  • Developed a Class-wise Focal Loss Variational AutoEncoder (CFLVAE) model for generating synthetic data samples for minority attack classes.
  • Designed a Class-wise Focal Loss (CFL) objective function to train the VAE, focusing on minority class samples and scrutinizing high-level features.
  • Trained a Deep Neural Network (DNN) classifier with a unique architecture on the balanced dataset generated by CFLVAE.
  • Evaluated the CFLVAE-DNN model using the challenging and imbalanced NSL-KDD intrusion detection dataset.

Main Results:

  • The CFLVAE effectively generated realistic and diverse intrusion data, creating a well-balanced dataset.
  • The CFLVAE-DNN model achieved superior intrusion detection performance, outperforming state-of-the-art methods.
  • Achieved 88.08% overall intrusion detection accuracy and a 3.77% false positive rate.
  • Demonstrated significant improvements in detecting low-frequency attacks, achieving 79.25% for U2R and 67.5% for R2L attacks.

Conclusions:

  • The proposed CFLVAE model successfully addresses the data imbalance problem in intrusion detection by generating high-quality synthetic data.
  • The CFLVAE-DNN approach significantly enhances intrusion detection accuracy and reduces false positives, particularly for rare attack types.
  • This generative approach offers a promising solution for improving the robustness and effectiveness of intrusion detection systems in real-world, imbalanced network environments.