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ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Network security is critical due to rising cyber threats.
  • Existing intrusion detection systems (IDS) struggle with data preparation and identifying novel attacks.
  • There is a need for advanced methods to enhance IDS efficacy.

Purpose of the Study:

  • To propose a novel network intrusion detection model (ID-RDRL).
  • To enhance the identification of unknown network threats.
  • To improve the overall performance of intrusion detection systems.

Main Methods:

  • Utilized Recursive Feature Elimination (RFE) for optimal feature selection.
  • Employed a neural network for feature information extraction.
  • Trained a classifier using Deep Reinforcement Learning (DRL) for intrusion recognition.
  • Evaluated the model on the CSE-CIC-IDS2018 dataset.

Main Results:

  • The ID-RDRL model effectively selects an optimal subset of features.
  • Approximately 80% of redundant features were removed.
  • DRL successfully learned selected features, enhancing IDS performance for attack identification.
  • The model demonstrated strong performance in identifying network intrusions.

Conclusions:

  • The ID-RDRL model offers a robust solution for network intrusion detection.
  • The approach shows significant potential for improving cybersecurity in complex network environments.
  • Feature selection and DRL integration are key to enhancing IDS capabilities against evolving threats.